Synaptic depression enables neuronal gain control

By J. Rothman, L. Cathala, V. Steuber, R. Silver. 2009

235 citations recorded by the source corpus.

To act as computational devices, neurons must perform mathematical operations as they transform synaptic and modulatory input into output firing rate. Experiments and theory indicate that neuronal firing typically represents the sum of synaptic inputs, an additive operation, but multiplication of inputs is essential for many computations. Multiplication by a constant produces a change in the slope, or gain, of the input–output relationship, amplifying or scaling down the sensitivity of the neuron to changes in its input. Such gain modulation occurs in vivo, during contrast invariance of orientation tuning, attentional scaling, translation-invariant object recognition, auditory processing and coordinate transformations. Moreover, theoretical studies highlight the necessity of gain modulation in several of these tasks. Although potential cellular mechanisms for gain modulation have been identified, they often rely on membrane noise and require restrictive conditions to work. Because nonlinear components are used to scale signals in electronics, we examined whether synaptic nonlinearities are involved in neuronal gain modulation. We used synaptic stimulation and the dynamic-clamp technique to investigate gain modulation in granule cells in acute slices of rat cerebellum. Here we show that when excitation is mediated by synapses with short-term depression (STD), neuronal gain is controlled by an inhibitory conductance in a noise-independent manner, allowing driving and modulatory inputs to be multiplied together. The nonlinearity introduced by STD transforms inhibition-mediated additive shifts in the input–output relationship into multiplicative gain changes. When granule cells were driven with bursts of high-frequency mossy fibre input, as observed in vivo, larger inhibition-mediated gain changes were observed, as expected with greater STD. Simulations of synaptic integration in more complex neocortical neurons suggest that STD-based gain modulation can also operate in neurons with large dendritic trees. Our results establish that neurons receiving depressing excitatory inputs can act as powerful multiplicative devices even when integration of postsynaptic conductances is linear.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “10 milliseconds, an order of magnitude larger than the timescale for synaptic integration of granule cells (D’Angelo et al., 1995; Schwartz et al., 2012; Rothman et al., 2009; Billings et al 2014).”
Short-term presynaptic plasticity. (2012, unknown): “This is illustrated by considering input/output curves at mossy fiber-to-granule cell synapses (Rothman et al. 2009).”
Handbook of ion channels (2015, background): “Here receptor desensitization underlies synaptic depression, allowing gain control and hence computation (Rothman et al., 2009).”
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling (2009, background): “It has been suggested that biophysical mechanisms such as shunting inhibition and synaptic depression might account for normalization and gain control [10, 21, 22].”
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, background): “0610(29) mS mm(22) and capacitance 1610(28) mF mm(22) received synaptic inputs whose strength and relative weight had been adjusted so as to match experimental voltage clamp data [8].”
Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding (2014, methodology): “…plasticity (STP) (Saviane and Silver, 2006), we converted existing experimental measurements of Poisson trains of synaptic currents to conductances (Rothman et al., 2009; Schwartz et al., 2012) and used them to constrain STP models of synaptic AMPAR and NMDAR components (Figures 7D and 7E,…”
NMDA receptors with incomplete Mg2+ block enable low-frequency transmission through the cerebellar cortex (2012, methodology): “The time courses of the AMPAR and NMDAR components were derived from fits to experimental data (Rothman et al., 2009; DiGregorio et al., 2002, 2007) and match the behavior of the AMPAR and NMDAR A1 A2 B C D E1 E2 F1 F2 Figure 8.”
Silencing the majority of cerebellar granule cells uncovers their essential role in motor learning and consolidation. (2013, unknown): “GCs’ unique design is probably essential to confer them high sensitivity for individual afferents (Chadderton et al., 2004; Ekerot and Jörntell, 2008; Rothman et al., 2009).”
Rapid Desynchronization of an Electrically Coupled Interneuron Network with Sparse Excitatory Synaptic Input (2010, unknown): “Nevertheless, variations in inhibitory input synchrony may be a dynamic way to alter granule cell gain during sensory input, in addition to the steady-state tonic inhibitory component (Crowley et al., 2009; Mitchell and Silver, 2003; Rothman et al., 2009).”
Controlling Recurrent Neural Networks by Conceptors (2014, background): “For a multiplicative modulation of neuronal activity a number of biological mechanisms have been proposed, for example [92, 14].”
Purkinje cells directly inhibit granule cells in specialized regions of the cerebellar cortex (2016, unknown): “Functional Roles of PC to GrC Inhibition Previous studies established that fast and slow GoC to GrC inhibition and tonic inhibition reduce GrC excitability (Crowley et al., 2009; Mitchell and Silver, 2003; Rothman et al., 2009).”