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Synaptic depression enables neuronal gain control
J.S. Rothman
, L. Cathala
,
Volker Steuber
, R.A. Silver
Centre for AI and Robotics Research
Department of Computer Science
School of Physics, Engineering & Computer Science
Centre of Data Innovation Research
Biocomputation Research Group
Research output
:
Contribution to journal
›
Article
›
peer-review
183
Citations (Scopus)
Overview
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Keyphrases
Synaptic Depression
100%
Neural Gain
100%
Gain Modulation
100%
Gain Control
100%
Long-term Depression
66%
Gain Change
33%
Nonlinearity
33%
Synaptic Input
33%
Input-output Relation
33%
Granule Cell
33%
Conductance
33%
Modulatory Input
33%
Synapse
16%
Spontaneous Excitatory Postsynaptic Current (sEPSC)
16%
Auditory Processing
16%
Synaptic Integration
16%
Mossy Fiber
16%
Excitatory Input
16%
Restrictive Conditions
16%
Dendritic Tree
16%
Nonlinear Component
16%
Synaptic Activity
16%
Clamping Method
16%
Firing Rate
16%
Contrast Invariance
16%
Invariant Object Recognition
16%
Acute Slice
16%
Rat Cerebellum
16%
Additive Shift
16%
Mathematical Operations
16%
Translation Invariant
16%
Synaptic
16%
Computational Devices
16%
Multiplicative Gain Variation
16%
Multiplication by a Constant
16%
Cellular Mechanisms
16%
Downscaling
16%
Noise-independent
16%
Driving Input
16%
Orientation Tuning
16%
Dynamic Clamp
16%
Neocortical Neurons
16%
Attentional Scaling
16%
Additive Surgery
16%
Neuronal Firing
16%
Coordinate Transformation
16%
Membrane Noise
16%
Neuroscience
Granule Cell
100%
In Vivo
100%
Cell Membrane Potential
50%
Cerebellum
50%
Synapse
50%
Firing Rate
50%