Network dynamics underlying OFF responses in the auditory cortex

AbstractAcross sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of network dynamics and propose a network model that generates transient OFF responses through recurrent interactions. To test this model, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli. We found that the network model accounts for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activate mostly orthogonal dimensions in the neural state-space. In contrast, a single-cell mechanism explains some prominent features of the data, but does not account for the structure across stimuli and trials captured by the network model.

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Network dynamics underlying OFF responses in the auditory cortex

eLife ◽

10.7554/elife.53151 ◽

2021 ◽

Vol 10 ◽

Author(s):

Giulio Bondanelli ◽

Thomas Deneux ◽

Brice Bathellier ◽

Srdjan Ostojic

Keyword(s):

Auditory Cortex ◽

Single Cell ◽

Cell Model ◽

Network Models ◽

Population Responses ◽

Two Photon ◽

Cortical Areas ◽

Single Cell Model ◽

Spatio Temporal ◽

Low Dimensional

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

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Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain

10.1101/059840 ◽

2016 ◽

Author(s):

Irina Higgins ◽

Simon Stringer ◽

Jan Schnupp

Keyword(s):

Neural Network ◽

Auditory Cortex ◽

Network Model ◽

Neural Network Model ◽

Spiking Neural Network ◽

Cortical Areas ◽

Speaker Independent ◽

Temporal Encoding ◽

Spatio Temporal ◽

Spiking Neural Network Model

AbstractThe nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.Author SummaryCurrently we still do not know how the auditory cortex encodes the identity of complex auditory objects, such as words, given the great variability in the raw auditory waves that correspond to the different pronunciations of the same word by different speakers. Here we argue for temporal information encoding within neural cell assemblies for representing auditory objects. Unlike the more traditionally accepted rate encoding, temporal encoding takes into account the precise relative timing of spikes across a population of neurons. We provide support for our hypothesis by building a neurophysiologically grounded spiking neural network model of the auditory brain with a biologically plausible learning mechanism. We show that the model learns to differentiate between naturally spoken digits “one” and “two” pronounced by numerous speakers in a speaker-independent manner through simple unsupervised exposure to the words. Our simulations demonstrate that temporal encoding contains significantly more information about the two words than rate encoding. We also show that such learning depends on the presence of stable patterns of firing in the input to the cortical areas of the model that are performing the learning.

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Heterogeneous Associative Plasticity in the Auditory Cortex Induced by Fear Learning – Novel Insight Into the Classical Conditioning Paradigm

Physiological Research ◽

10.33549/physiolres.934559 ◽

2021 ◽

pp. 447-460

Author(s):

O ZELENKA ◽

O NOVAK ◽

A BRUNOVA ◽

J SYKA

Keyword(s):

Auditory Cortex ◽

Single Cell ◽

Escape Behavior ◽

Stimulus Frequency ◽

Fear Learning ◽

Conditioning Paradigm ◽

Two Photon ◽

Simple Expansion ◽

Insight Into ◽

Conventional Freezing

We used two-photon calcium imaging with single-cell and cell-type resolution. Fear conditioning induced heterogeneous tuning shifts at single-cell level in the auditory cortex, with shifts both to CS+ frequency and to the control CS- stimulus frequency. We thus extend the view of simple expansion of CS+ tuned regions. Instead of conventional freezing reactions only, we observe selective orienting responses towards the conditioned stimuli. The orienting responses were often followed by escape behavior.

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Ultrafast Light Targeting for High-Throughput Precise Control of Neuronal Networks

10.1101/2021.06.14.448315 ◽

2021 ◽

Author(s):

Giulia Faini ◽

Clement Molinier ◽

Cecile Telliez ◽

Christophe Tourain ◽

Benoit C Forget ◽

...

Keyword(s):

Single Cell ◽

Activity Patterns ◽

Optical Control ◽

Temporal Control ◽

Precise Control ◽

Neuronal Ensembles ◽

Two Photon ◽

Spatio Temporal ◽

Focused Beams ◽

Ability To Drive

Understanding how specific sets of neurons fire and wire together during cognitive-relevant activity is one of the most pressing questions in neuroscience. Two-photon, single-cell resolution optogenetics based on holographic light-targeting approaches enables accurate spatio-temporal control of individual or multiple neurons. Yet, currently, the ability to drive asynchronous activity in distinct cells is critically limited to a few milliseconds and the achievable number of targets to several dozens. In order to expand the capability of single-cell optogenetics, we introduce an approach capable of ultra-fast sequential light targeting (FLiT), based on switching temporally focused beams between holograms at kHz rates. We demonstrate serial-parallel photostimulation strategies capable of multi-cell sub-millisecond temporal control and many-fold expansion of the number of activated cells. This approach will be important for experiments that require rapid and precise cell stimulation with defined spatio-temporal activity patterns and optical control of large neuronal ensembles.

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Competition and convergence between auditory and cross-modal visual inputs to primary auditory cortical areas

Journal of Neurophysiology ◽

10.1152/jn.00407.2010 ◽

2011 ◽

Vol 105 (4) ◽

pp. 1558-1573 ◽

Cited By ~ 13

Author(s):

Yu-Ting Mao ◽

Tian-Miao Hua ◽

Sarah L. Pallas

Keyword(s):

Auditory Cortex ◽

Multisensory Processing ◽

Sensory Deprivation ◽

Target Space ◽

Target Cells ◽

Auditory Input ◽

Visual Neurons ◽

Cortical Areas ◽

Visual Inputs ◽

Medial Geniculate

Sensory neocortex is capable of considerable plasticity after sensory deprivation or damage to input pathways, especially early in development. Although plasticity can often be restorative, sometimes novel, ectopic inputs invade the affected cortical area. Invading inputs from other sensory modalities may compromise the original function or even take over, imposing a new function and preventing recovery. Using ferrets whose retinal axons were rerouted into auditory thalamus at birth, we were able to examine the effect of varying the degree of ectopic, cross-modal input on reorganization of developing auditory cortex. In particular, we assayed whether the invading visual inputs and the existing auditory inputs competed for or shared postsynaptic targets and whether the convergence of input modalities would induce multisensory processing. We demonstrate that although the cross-modal inputs create new visual neurons in auditory cortex, some auditory processing remains. The degree of damage to auditory input to the medial geniculate nucleus was directly related to the proportion of visual neurons in auditory cortex, suggesting that the visual and residual auditory inputs compete for cortical territory. Visual neurons were not segregated from auditory neurons but shared target space even on individual target cells, substantially increasing the proportion of multisensory neurons. Thus spatial convergence of visual and auditory input modalities may be sufficient to expand multisensory representations. Together these findings argue that early, patterned visual activity does not drive segregation of visual and auditory afferents and suggest that auditory function might be compromised by converging visual inputs. These results indicate possible ways in which multisensory cortical areas may form during development and evolution. They also suggest that rehabilitative strategies designed to promote recovery of function after sensory deprivation or damage need to take into account that sensory cortex may become substantially more multisensory after alteration of its input during development.

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Temporal property of single-cell activity in response to motion-defined shapes in monkey dorsal and ventral cortical areas

Neuroreport ◽

10.1097/wnr.0000000000000826 ◽

2017 ◽

Vol 28 (13) ◽

pp. 793-799 ◽

Cited By ~ 2

Author(s):

Takashi Handa ◽

Shumpei Unno ◽

Akichika Mikami

Keyword(s):

Single Cell ◽

Cell Activity ◽

Temporal Property ◽

Cortical Areas ◽

Single Cell Activity

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Persistent activity and the single-cell frequency–current curve in a cortical network model

Network Computation in Neural Systems ◽

10.1088/0954-898x_11_4_302 ◽

2000 ◽

Vol 11 (4) ◽

pp. 261-280 ◽

Cited By ~ 24

Author(s):

Nicolas Brunel

Keyword(s):

Single Cell ◽

Network Model ◽

Cortical Network ◽

Persistent Activity ◽

Cell Frequency ◽

Current Curve ◽

Frequency Current

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Learning spatio-temporal visual features by a large scale neural network model

Neuroscience Research ◽

10.1016/j.neures.2010.07.1925 ◽

2010 ◽

Vol 68 ◽

pp. e434

Author(s):

Naoto Yukinawa ◽

Shin Ishii

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Large Scale ◽

Visual Features ◽

Spatio Temporal

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Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

Journal of Neurophysiology ◽

10.1152/jn.00845.2009 ◽

2011 ◽

Vol 105 (2) ◽

pp. 757-778 ◽

Cited By ~ 17

Author(s):

Malte J. Rasch ◽

Klaus Schuch ◽

Nikos K. Logothetis ◽

Wolfgang Maass

Keyword(s):

Network Model ◽

Network Models ◽

Cortical Network ◽

Inhibitory Neurons ◽

Sensory Stimuli ◽

Natural Stimuli ◽

Cortical Areas ◽

Model Response ◽

Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

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