Literature DB >> 25601482

Effects of spike-triggered negative feedback on receptive-field properties.

Eugenio Urdapilleta1, Inés Samengo.   

Abstract

Sensory neurons are often described in terms of a receptive field, that is, a linear kernel through which stimuli are filtered before they are further processed. If information transmission is assumed to proceed in a feedforward cascade, the receptive field may be interpreted as the external stimulus' profile maximizing neuronal output. The nervous system, however, contains many feedback loops, and sensory neurons filter more currents than the ones representing the transduced external stimulus. Some of the additional currents are generated by the output activity of the neuron itself, and therefore constitute feedback signals. By means of a time-frequency analysis of the input/output transformation, here we show how feedback modifies the receptive field. The model is applicable to various types of feedback processes, from spike-triggered intrinsic conductances to inhibitory synaptic inputs from nearby neurons. We distinguish between the intrinsic receptive field (filtering all input currents) and the effective receptive field (filtering only external stimuli). Whereas the intrinsic receptive field summarizes the biophysical properties of the neuron associated to subthreshold integration and spike generation, only the effective receptive field can be interpreted as the external stimulus' profile maximizing neuronal output. We demonstrate that spike-triggered feedback shifts low-pass filtering towards band-pass processing, transforming integrator neurons into resonators. For strong feedback, a sharp resonance in the spectral neuronal selectivity may appear. Our results provide a unified framework to interpret a collection of previous experimental studies where specific feedback mechanisms were shown to modify the filtering properties of neurons.

Mesh:

Year:  2015        PMID: 25601482     DOI: 10.1007/s10827-014-0546-0

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  71 in total

1.  Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro.

Authors:  M V Sanchez-Vives; L G Nowak; D A McCormick
Journal:  J Neurosci       Date:  2000-06-01       Impact factor: 6.167

Review 2.  Resonance, oscillation and the intrinsic frequency preferences of neurons.

Authors:  B Hutcheon; Y Yarom
Journal:  Trends Neurosci       Date:  2000-05       Impact factor: 13.837

3.  Dynamic modification of cortical orientation tuning mediated by recurrent connections.

Authors:  Gidon Felsen; Yao-song Shen; Haishan Yao; Gareth Spor; Chaoyi Li; Yang Dan
Journal:  Neuron       Date:  2002-12-05       Impact factor: 17.173

4.  Adaptation without parameter change: Dynamic gain control in motion detection.

Authors:  Alexander Borst; Virginia L Flanagin; Haim Sompolinsky
Journal:  Proc Natl Acad Sci U S A       Date:  2005-04-15       Impact factor: 11.205

Review 5.  Visual adaptation: physiology, mechanisms, and functional benefits.

Authors:  Adam Kohn
Journal:  J Neurophysiol       Date:  2007-03-07       Impact factor: 2.714

Review 6.  State-dependent computations: spatiotemporal processing in cortical networks.

Authors:  Dean V Buonomano; Wolfgang Maass
Journal:  Nat Rev Neurosci       Date:  2009-01-15       Impact factor: 34.870

7.  How adaptation currents change threshold, gain, and variability of neuronal spiking.

Authors:  Josef Ladenbauer; Moritz Augustin; Klaus Obermayer
Journal:  J Neurophysiol       Date:  2013-10-30       Impact factor: 2.714

8.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex.

Authors:  D J Amit; N Brunel
Journal:  Cereb Cortex       Date:  1997 Apr-May       Impact factor: 5.357

9.  Excitatory and inhibitory interactions in localized populations of model neurons.

Authors:  H R Wilson; J D Cowan
Journal:  Biophys J       Date:  1972-01       Impact factor: 4.033

Review 10.  Normalization as a canonical neural computation.

Authors:  Matteo Carandini; David J Heeger
Journal:  Nat Rev Neurosci       Date:  2011-11-23       Impact factor: 34.870

View more
  2 in total

1.  Firing-rate resonances in the peripheral auditory system of the cricket, Gryllus bimaculatus.

Authors:  Florian Rau; Jan Clemens; Victor Naumov; R Matthias Hennig; Susanne Schreiber
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2015-08-21       Impact factor: 1.836

2.  Active dendrites mediate stratified gamma-range coincidence detection in hippocampal model neurons.

Authors:  Anindita Das; Rishikesh Narayanan
Journal:  J Physiol       Date:  2015-06-25       Impact factor: 5.182

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.