What can we learn from synaptic weight distributions?

Barbour, Boris; Brunel, Nicolas; Hakim, Vincent; Nadal, Jean-Pierre

doi:10.1016/j.tins.2007.09.005

Cited by 170 publications

(170 citation statements)

References 60 publications

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“…Subcriticality of the network and nonlinearity in the input summation have been proposed as possible explanations for the heavy tails of the experimental distributions of connection weights (34). Here, we examine an alternative possibility.…”

Section: Comparison Of Theoretical Predictions With Experimental Measmentioning

confidence: 92%

“…With this approximation, it is possible to decouple the recurrent network into a set of independent perceptron-like units (Fig. 1A) (34). We also assume that these units are functioning at their critical capacities, which enables us to find a unique analytic solution, independent of the details of the learning rule.…”

Section: Methodsmentioning

confidence: 99%

“…Although connections between inhibitory and excitatory neurons are comparatively more frequent (29,31), their probabilities also are well below 100%. Lastly, we note that the distributions of excitatory and inhibitory connection weights, measured in terms of the amplitudes of unitary postsynaptic potentials (PSPs), have a stereotypic shape (28)(29)(30)(32)(33)(34). This shape can be described as a truncated Gaussian, except for a somewhat heavier tail.…”

mentioning

confidence: 97%

“…Notable exceptions are the studies by Brunel et al (32) and Barbour et al (34) that analyzed associative memory storage in a model of a Purkinje cell receiving excitatory parallel fiber inputs. These works show that, at the maximum (critical) storage capacity, the probability of finding a functional parallel fiber to Purkinje cell connection is less than 50% (32,34). In other words, the majority of parallel fibers that are potentially connected to a given Purkinje cell does not establish functional connections (i.e., connections are either silent or absent).…”

mentioning

confidence: 99%

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Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons

Chapeton

Fares

LaSota

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Many features of synaptic connectivity are ubiquitous among cortical systems. Cortical networks are dominated by excitatory neurons and synapses, are sparsely connected, and function with stereotypically distributed connection weights. We show that these basic structural and functional features of synaptic connectivity arise readily from the requirement of efficient associative memory storage. Our theory makes two fundamental predictions. First, we predict that, despite a large number of neuron classes, functional connections between potentially connected cells must be realized with <50% probability if the presynaptic cell is excitatory and >50% probability if the presynaptic cell is inhibitory. Second, we establish a unique relation between probability of connection and coefficient of variation in connection weights. These predictions are consistent with a dataset of 74 published experiments reporting connection probabilities and distributions of postsynaptic potential amplitudes in various cortical systems. What is more, our theory explains the shapes of the distributions obtained in these experiments.learning and memory | cortical connectivity | synaptic weight | perceptron | critical capacity F undamental functions of the brain, such as learning and memory storage, are mediated by many mechanisms of excitatory (1-4) and inhibitory (5-9) synaptic plasticity. Working together with the genetically encoded developmental mechanisms of circuit formation, synaptic plasticity shapes neural circuits by creating, modifying, and eliminating individual synaptic connections in an experience-dependent manner. It is, therefore, reasonable to hypothesize that many stereotypic features of adult synaptic connectivity, whether established through evolution or the developmental learning process, have arisen to facilitate memory storage.In this study, we focus on three such features of cortical connectivity. Cortical connectivity is predominantly excitatory; it is mediated by two major classes of neurons-excitatory glutamatergic and inhibitory GABAergic cells. Chemical synapses made by the axons of inhibitory cells in the adult brain are believed to be all inhibitory, whereas those synapses made by the axons of excitatory neurons are believed to be all excitatory (10). The resulting connectivity is largely excitatory, with only about 15-20% of inhibitory neurons and inhibitory synapses (11). The second stereotypic feature of cortical connectivity is sparseness. Networks in the cortex are thought to be organized into relatively small units ranging from hundreds to tens of thousands of neurons in size. Such units may include mini columns (12, 13), structural columns (14, 15), and a variety of functional columns (16,17). Analysis of neuron morphology (14,(18)(19)(20)(21) has shown that cells within such units have the potential of being connected by structural synaptic plasticity (22-24). However, despite this potential, synaptic connectivity within the units is sparse. For example, nearby excitatory neurons in the neocortex are sy...

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Section: Comparison Of Theoretical Predictions With Experimental Measmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

See 2 more Smart Citations

Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons

Chapeton

Fares

LaSota

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Several other synapses appear to have key involvement in pattern detection, but in this case one particular synapse is much more important, as its weight saturates to the maximum value of w/D=1. This results in a final synaptic weight distribution that is bimodal, and indicative of an additive rule [41]. Other analysis factors such as population firing rate and average firing rates appeared roughly the same in both simulations.…”

Section: Discussionmentioning

confidence: 79%

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

Cantley

Ivans

Subramaniam

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates

et al. 2016

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Flexible metal oxide/graphene oxide hybrid multi-gate neuromorphic transistors are fabricated on flexible conducting graphene substrates. Dendritic integrations in both spatial and temporal modes are emulated, and spatiotemporal correlated logics are obtained. A proof-of-principle visual system model for emulating Lobula Giant Motion Detector neuron is also investigated. The results are of great significance for flexible sensors and neuromorphic cognitive systems.

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What can we learn from synaptic weight distributions?

Cited by 170 publications

References 60 publications

Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons

Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates

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