Inhibition, not excitation, is the key to multimodal sensory integration

Friedel, Paul; Hemmen, J. Leo van

doi:10.1007/s00422-008-0236-y

Cited by 15 publications

(18 citation statements)

References 54 publications

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“…Theoretical studies have confirmed that excitatory and inhibitory teaching input can account for proper map alignment and thus development of multimodal space (Friedel and van Hemmen 2008;Davison and Frégnac 2006). It is, however, only by inhibitory teaching input that an already aligned map can be re-aligned later on (Friedel and van Hemmen 2008).…”

Section: Development Of Multisensory Spacementioning

confidence: 89%

“…Although the precise nature of this teaching signal has not been clarified experimentally, selective neuronal disinhibition, or gating, seems to play a key role (Gutfreund et al 2002;Winkowski and Knudsen 2006). Theoretical studies have confirmed that excitatory and inhibitory teaching input can account for proper map alignment and thus development of multimodal space (Friedel and van Hemmen 2008;Davison and Frégnac 2006). It is, however, only by inhibitory teaching input that an already aligned map can be re-aligned later on (Friedel and van Hemmen 2008).…”

Section: Development Of Multisensory Spacementioning

confidence: 91%

“…Here the correct synaptic connections have to be learned. It has been shown (Franosch et al 2005a;Friedel and van Hemmen 2008) that a teacher such as the visual system can generate correct synaptic strengths so that a map can indeed develop in other modalities by means of (supervised) STDP; for details see Sect. 5.2.…”

Section: Neuronal Realization Of the Modelmentioning

confidence: 97%

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Optimality in mono- and multisensory map formation

et al. 2010

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In the struggle for survival in a complex and dynamic environment, nature has developed a multitude of sophisticated sensory systems. In order to exploit the information provided by these sensory systems, higher vertebrates reconstruct the spatio-temporal environment from each of the sensory systems they have at their disposal. That is, for each modality the animal computes a neuronal representation of the outside world, a monosensory neuronal map. Here we present a universal framework that allows to calculate the specific layout of the involved neuronal network by means of a general mathematical principle, viz., stochastic optimality. In order to illustrate the use of this theoretical framework, we provide a step-by-step tutorial of how to apply our model. In so doing, we present a spatial and a temporal example of optimal stimulus reconstruction which underline the advantages of our approach. That is, given a known physical signal transmission and rudimental knowledge of the detection process, our approach allows to estimate the possible performance and to predict neuronal properties of biological sensory systems. Finally, information from different sensory modalities has to be integrated so as to gain a unified perception of reality for further processing, e.g., for distinct motor commands. We briefly discuss concepts of multimodal interaction and how a multimodal space can evolve by alignment of monosensory maps.

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Section: Development Of Multisensory Spacementioning

confidence: 89%

Section: Development Of Multisensory Spacementioning

confidence: 91%

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Optimality in mono- and multisensory map formation

et al. 2010

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“…In previous models of collicular map formation based on Hebbian plasticity (18,25), temporal correlations and response latencies were not considered, leaving out the potential significance of delayed inputs for instructive coding. By contrast, we interpret latency differences as a computational strategy of the brain, agreeing with the notion that latency coding is very prominent in the visual system and can be found as early as in the retina (26).…”

Section: Discussionmentioning

confidence: 99%

“…A special case of instructive coding, cross-modal spatial transformations, can be formed by spike-time-dependent plasticity (STDP) rules when driven by multimodal inputs (17,18). Although STDP by itself does not seem to be capable of supporting arbitrary instructive coding (19), we identify a possible scenario for the implementation of the perceptron rule, namely, in cells that display both SFA and STDP.…”

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confidence: 95%

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity

D’Souza

Liu²,

Hahnloser³

2010

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

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It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-timedependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensoryguided processing.delta learning rule | Hebbian learning | sensory fusion | synaptic potentiation | supervised M any of the sensory and motor tasks solved by the brain can be captured in simple equations or minimization criteria. For example, minimization of errors made during reconstruction of natural images using sparse priors leads to linear filters reminiscent of simple cells (1, 2), minimization of retinal slip or visual error leads to emergence and maintenance of neural integrator networks (3-5), and optimality criteria derived from information theory can model the remapping dynamics of receptive fields in the barn owl midbrain (6).Despite these advances, little is known about cellular physiological properties that could serve as primitives for solving such computational tasks. Among the known primitives are short-term synaptic depression, which can give rise to multiplicative gain control (7), or spike-frequency adaptation (SFA), which may provide high-pass filtering of sensory inputs (8, 9).Here, we explore biophysical mechanisms and computational primitives for instructive coding. Instructive coding is a computation that allows the brain to constrain its sensory representations adaptively by exploiting intrinsic properties of the physical world. The example we consider here is that sound sources and salient visual stimuli often co-localize (e.g., when a dried branch cracks under the footstep of an animal). In the barn owl, a highly efficient predator, this auditory-visual co-localization is well reflected by registration of auditory and visual maps in the external nucleus of the inferior colliculus (ICX) and the optic tectum (OT). The instructive aspect of this registration is that it is actively maintained by plasticity mechanisms: When the visual field of owls is chronically shifted by prisms, neurons in ICX and OT develop a shift in their auditory receptive fields that corresponds to the visual field displacement (10, 11). Hence, visual inputs to these areas are ...

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Hydrodynamic Object Formation: Perception, Neuronal Representation, and Multimodal Integration

Hemmen

2014

Flow Sensing in Air and Water

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Inhibition, not excitation, is the key to multimodal sensory integration

Cited by 15 publications

References 54 publications

Optimality in mono- and multisensory map formation

Optimality in mono- and multisensory map formation

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity

Hydrodynamic Object Formation: Perception, Neuronal Representation, and Multimodal Integration

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