Delayed excitatory and inhibitory feedback shape neural information transmission.

Feedback circuitry with conduction and synaptic delays is ubiquitous in the nervous system. Yet the effects of delayed feedback on sensory processing of natural signals are poorly understood. This study explores the consequences of delayed excitatory and inhibitory feedback inputs on the processing of sensory information. We show, through numerical simulations and theory, that excitatory and inhibitory feedback can alter the firing frequency response of stochastic neurons in opposite ways by creating dynamical resonances, which in turn lead to information resonances (i.e., increased information transfer for specific ranges of input frequencies). The resonances are created at the expense of decreased information transfer in other frequency ranges. Using linear response theory for stochastically firing neurons, we explain how feedback signals shape the neural transfer function for a single neuron as a function of network size. We also find that balanced excitatory and inhibitory feedback can further enhance information tuning while maintaining a constant mean firing rate. Finally, we apply this theory to in vivo experimental data from weakly electric fish in which the feedback loop can be opened. We show that it qualitatively predicts the observed effects of inhibitory feedback. Our study of feedback excitation and inhibition reveals a possible mechanism by which optimal processing may be achieved over selected frequency ranges.

[1]  L Maler,et al.  The nucleus praeeminentialis: A Golgi study of a feedback center in the electrosensory system of gymnotid fish , 1983, The Journal of comparative neurology.

[2]  Ramón y Cajal,et al.  Histologie du système nerveux de l'homme & des vertébrés , 1909 .

[3]  M. Chacron,et al.  Integrate-and-fire neurons with threshold noise: a tractable model of how interspike interval correlations affect neuronal signal transmission. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Riccardo Mannella,et al.  Stochastic resonance in perspective , 1995 .

[5]  L. Maler,et al.  Distal versus proximal inhibitory shaping of feedback excitation in the electrosensory lateral line lobe: implications for sensory filtering. , 1998, Journal of neurophysiology.

[6]  Maurice J Chacron,et al.  Receptive Field Organization Determines Pyramidal Cell Stimulus-Encoding Capability and Spatial Stimulus Selectivity , 2002, The Journal of Neuroscience.

[7]  T. Sejnowski,et al.  Control of Spatiotemporal Coherence of a Thalamic Oscillation by Corticothalamic Feedback , 1996, Science.

[8]  L. Maler,et al.  The cytology of the posterior lateral line lobe of high‐frequency weakly electric fish (gymnotidae): Dendritic differentiation and synaptic specificity in a simple cortex , 1981, The Journal of comparative neurology.

[9]  Stephen Coombes,et al.  Dynamics of Strongly Coupled Spiking Neurons , 2000, Neural Computation.

[10]  G. Laurent,et al.  Distinct Mechanisms for Synchronization and Temporal Patterning of Odor-Encoding Neural Assemblies , 1996, Science.

[11]  Maurizio Mattia,et al.  Finite-size dynamics of inhibitory and excitatory interacting spiking neurons. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  André Longtin,et al.  Stochastic resonance in models of neuronal ensembles , 1997 .

[13]  L. Maler,et al.  Neural architecture of the electrosensory lateral line lobe: adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering , 1999, The Journal of experimental biology.

[14]  Nicolas Brunel,et al.  Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates , 1999, Neural Computation.

[15]  André Longtin,et al.  Noise shaping by interval correlations increases information transfer. , 2004, Physical review letters.

[16]  Brent Doiron,et al.  Non-classical receptive field mediates switch in a sensory neuron's frequency tuning , 2003, Nature.

[17]  Nicolas Brunel,et al.  Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons , 2002, Neural Computation.

[18]  Frances S. Chance,et al.  Gain Modulation from Background Synaptic Input , 2002, Neuron.

[19]  Nicolas Brunel,et al.  Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.

[20]  L Schimansky-Geier,et al.  Transmission of noise coded versus additive signals through a neuronal ensemble. , 2001, Physical review letters.

[21]  D. Wilkin,et al.  Neuron , 2001, Brain Research.

[22]  Maurice J Chacron,et al.  Feedback and Feedforward Control of Frequency Tuning to Naturalistic Stimuli , 2005, The Journal of Neuroscience.

[23]  Jonghan Shin,et al.  Adaptation in spiking neurons based on the noise shaping neural coding hypothesis , 2001, Neural Networks.

[24]  Christopher L Passaglia,et al.  Information transmission rates of cat retinal ganglion cells. , 2004, Journal of neurophysiology.

[25]  Brent Doiron,et al.  Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli , 2003, Nature.

[26]  L. Maler,et al.  Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors. , 2000, Physical review letters.

[27]  George L. Gerstein,et al.  Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex , 1994, Nature.

[28]  Daniel J. Amit,et al.  Quantitative Study of Attractor Neural Network Retrieving at Low Spike Rates: I , 1991 .

[29]  C. Koch,et al.  Coding of time-varying electric field amplitude modulations in a wave-type electric fish. , 1996, Journal of neurophysiology.

[30]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[31]  L. Maler,et al.  Plastic and Nonplastic Pyramidal Cells Perform Unique Roles in a Network Capable of Adaptive Redundancy Reduction , 2004, Neuron.

[32]  Fabrizio Gabbiani,et al.  Burst firing in sensory systems , 2004, Nature Reviews Neuroscience.

[33]  André Longtin,et al.  Dynamics of Deterministic and Stochastic Paired ExcitatoryInhibitory Delayed Feedback , 2003, Neural Computation.

[34]  I. Goychuk,et al.  Information transfer with rate-modulated Poisson processes: a simple model for nonstationary stochastic resonance. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[36]  Leonard Maler,et al.  The organization of afferent input to the caudal lobe of the cerebellum of the gymnotid fish Apteronotus leptorhynchus , 2004, Anatomy and Embryology.

[37]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[38]  Carson C. Chow,et al.  Noise shaping in populations of coupled model neurons. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Carl van Vreeswijk,et al.  Patterns of Synchrony in Neural Networks with Spike Adaptation , 2001, Neural Computation.

[40]  J. Alves-Gomes,et al.  Systematic biology of gymnotiform and mormyriform electric fishes: phylogenetic relationships, molecular clocks and rates of evolution in the mitochondrial rRNA genes , 1999, The Journal of experimental biology.

[41]  M. Steriade.  Coherent oscillations and short-term plasticity in corticothalamic networks , 1999, Trends in Neurosciences.

[42]  Brent Doiron,et al.  Oscillatory activity in electrosensory neurons increases with the spatial correlation of the stochastic input stimulus. , 2004, Physical review letters.

[43]  J. Bastian.  Electrolocation: I. How the electroreceptors ofApteronotus albifrons code for moving objects and other electrical stimuli , 1981 .

[44]  Lutz Schimansky-Geier,et al.  STOCHASTIC RESONANCE : NOISE-ENHANCED PHASE COHERENCE , 1998 .

[45]  D. Ferster,et al.  The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. , 2000, Science.

[46]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[47]  John P. Miller,et al.  Assessing the Performance of Neural Encoding Models in the Presence of Noise , 2000, Journal of Computational Neuroscience.

[48]  J. Miller,et al.  Information theoretic analysis of dynamical encoding by four identified primary sensory interneurons in the cricket cercal system. , 1996, Journal of neurophysiology.

[49]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[50]  T. Sejnowski,et al.  Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models , 2000, The Journal of Neuroscience.

[51]  L. Maler,et al.  The posterior lateral line lobe of certain gymnotoid fish: Quantitative light microscopy , 1979, The Journal of comparative neurology.

[52]  H. Tuckwell.  Nonlinear and stochastic theories , 1988 .

[53]  W. Gerstner,et al.  Time structure of the activity in neural network models. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[54]  H. Risken.  Fokker-Planck Equation , 1996 .

[55]  M. Stemmler.  A single spike suffices: the simplest form of stochastic resonance in model neurons , 1996 .

[56]  Wiesenfeld,et al.  Theory of stochastic resonance. , 1989, Physical review. A, General physics.

[57]  Song Bai Park,et al.  Novel neural circuits based on stochastic pulse coding and noise feedback pulse coding , 1993 .

[58]  A. Treves.  Mean-field analysis of neuronal spike dynamics , 1993 .

[59]  J. Bastian.  Gain control in the electrosensory system mediated by descending inputs to the electrosensory lateral line lobe , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[60]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[61]  C A Shumway,et al.  Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. II. Anatomical differences , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[62]  Lutz Schimansky-Geier,et al.  Linear response theory applied to stochastic resonance in models of ensembles of oscillators , 1997 .

[63]  M. Mattia,et al.  Population dynamics of interacting spiking neurons. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.