scholarly journals Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

2017 ◽  
Vol 13 (3) ◽  
pp. e1005437 ◽  
Author(s):  
Tohar S. Yarden ◽  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Network Model ◽  
Primary Auditory Cortex ◽  
Recurrent Network ◽  
Specific Adaptation

Current Source Density Profiles of Stimulus-Specific Adaptation in Rat Auditory Cortex

2009 ◽  
Vol 102 (3) ◽  
pp. 1483-1490 ◽  
Author(s):  
Francois D. Szymanski ◽  
Jose A. Garcia-Lazaro ◽  
Jan W. H. Schnupp
Keyword(s):  
Auditory Cortex ◽  
Current Source ◽  
Primary Auditory Cortex ◽  
Auditory Pathway ◽  
Afferent Input ◽  
Current Source Density ◽  
Specific Adaptation ◽  
Source Density ◽  
Density Analysis ◽  
Layer V

Neurons in primary auditory cortex (A1) are known to exhibit a phenomenon known as stimulus-specific adaptation (SSA), which means that, when tested with pure tones, they will respond more strongly to a particular frequency if it is presented as a rare, unexpected “oddball” stimulus than when the same stimulus forms part of a series of common, “standard” stimuli. Although SSA has occasionally been observed in midbrain neurons that form part of the paraleminscal auditory pathway, it is thought to be weak, rare, or nonexistent among neurons of the leminscal pathway that provide the main afferent input to A1, so that SSA seen in A1 is likely generated within A1 by local mechanisms. To study the contributions that neural processing within the different cytoarchitectonic layers of A1 may make to SSA, we recorded local field potentials in A1 of the rat in response to standard and oddball tones and subjected these to current source density analysis. Although our results show that SSA can be observed throughout all layers of A1, right from the earliest part of the response, there are nevertheless significant differences between layers, with SSA becoming significantly stronger as stimulus-related activity passes from the main thalamorecipient layers III and IV to layer V.

scholarly journals A dynamic network model of temporal receptive fields in primary auditory cortex

2019 ◽  
Vol 15 (5) ◽  
pp. e1006618 ◽  
Author(s):  
Monzilur Rahman ◽  
Ben D. B. Willmore ◽  
Andrew J. King ◽  
Nicol S. Harper
Keyword(s):  
Auditory Cortex ◽  
Network Model ◽  
Receptive Fields ◽  
Dynamic Network ◽  
Primary Auditory Cortex ◽  
Dynamic Network Model

scholarly journals Recurrent network dynamics shape direction selectivity in primary auditory cortex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Destinee A. Aponte ◽  
Gregory Handy ◽  
Amber M. Kline ◽  
Hiroaki Tsukano ◽  
Brent Doiron ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Vocal Communication ◽  
Network Dynamics ◽  
Charge Asymmetry ◽  
Primary Auditory Cortex ◽  
Theoretical Models ◽  
Recurrent Network ◽  
Direction Selectivity ◽  
Complex Sounds ◽  
Recurrent Network Dynamics

AbstractDetecting the direction of frequency modulation (FM) is essential for vocal communication in both animals and humans. Direction-selective firing of neurons in the primary auditory cortex (A1) has been classically attributed to temporal offsets between feedforward excitatory and inhibitory inputs. However, it remains unclear how cortical recurrent circuitry contributes to this computation. Here, we used two-photon calcium imaging and whole-cell recordings in awake mice to demonstrate that direction selectivity is not caused by temporal offsets between synaptic currents, but by an asymmetry in total synaptic charge between preferred and non-preferred directions. Inactivation of cortical somatostatin-expressing interneurons (SOM cells) reduced direction selectivity, revealing its cortical contribution. Our theoretical models showed that charge asymmetry arises due to broad spatial topography of SOM cell-mediated inhibition which regulates signal amplification in strongly recurrent circuitry. Together, our findings reveal a major contribution of recurrent network dynamics in shaping cortical tuning to behaviorally relevant complex sounds.

scholarly journals AIM: A Network Model of Attention in Auditory Cortex

2020 ◽  
Author(s):  
Kenny F. Chou ◽  
Kamal Sen
Keyword(s):  
Auditory Cortex ◽  
Network Model ◽  
Frequency Tuning ◽  
Primary Auditory Cortex ◽  
Inhibitory Neurons ◽  
Cortical Circuits ◽  
Cocktail Party ◽  
Theoretical Frameworks ◽  
Cocktail Party Problem ◽  
Underlying Mechanisms

AbstractAttentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.

scholarly journals A dynamic network model can explain temporal receptive fields in primary auditory cortex

2018 ◽  
Author(s):  
Monzilur Rahman ◽  
Ben D. B. Willmore ◽  
Andrew J. King ◽  
Nicol S. Harper
Keyword(s):  
Auditory Cortex ◽  
Network Model ◽  
Time Delays ◽  
Receptive Fields ◽  
Dynamic Network ◽  
Primary Auditory Cortex ◽  
Neural Responses ◽  
The Past ◽  
Dynamic Network Model ◽  
History Of

ABSTRACTAuditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit’s response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.AUTHOR SUMMARYThe responses of neurons in the primary auditory cortex depend on the recent history of sounds over seconds or less. Typically, this dependence on the past has been modelled by applying a wide span of time delays to the input, although this is likely to be biologically unrealistic. Real neurons integrate the history of their activity due to the dynamical properties of their cell membranes and other components. We show that a network with a realistically narrow span of delays and with units having dynamic characteristics like those found in neurons, succinctly models neural responses recorded from ferret primary auditory cortex. Because these integrative properties are widespread, our dynamic network provides a basis for modelling responses in other neural systems.

scholarly journals AIM: A network model of attention in auditory cortex

2021 ◽  
Vol 17 (8) ◽  
pp. e1009356
Author(s):  
Kenny F. Chou ◽  
Kamal Sen
Keyword(s):  
Auditory Cortex ◽  
Network Model ◽  
Frequency Tuning ◽  
Primary Auditory Cortex ◽  
Inhibitory Neurons ◽  
Cortical Circuits ◽  
Cocktail Party ◽  
Theoretical Frameworks ◽  
Cocktail Party Problem ◽  
Underlying Mechanisms

Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.

scholarly journals Arousal regulates frequency tuning in primary auditory cortex

2019 ◽  
Vol 116 (50) ◽  
pp. 25304-25310 ◽  
Author(s):  
Pei-Ann Lin ◽  
Samuel K. Asinof ◽  
Nicholas J. Edwards ◽  
Jeffry S. Isaacson
Keyword(s):  
Auditory Cortex ◽  
Frequency Tuning ◽  
Primary Auditory Cortex ◽  
Pyramidal Cells ◽  
Frequency Discrimination ◽  
Recurrent Network ◽  
Excitatory Input ◽  
Network Activity ◽  
Shape Tuning

Changes in arousal influence cortical sensory representations, but the synaptic mechanisms underlying arousal-dependent modulation of cortical processing are unclear. Here, we use 2-photon Ca2+ imaging in the auditory cortex of awake mice to show that heightened arousal, as indexed by pupil diameter, broadens frequency-tuned activity of layer 2/3 (L2/3) pyramidal cells. Sensory representations are less sparse, and the tuning of nearby cells more similar when arousal increases. Despite the reduction in selectivity, frequency discrimination by cell ensembles improves due to a decrease in shared trial-to-trial variability. In vivo whole-cell recordings reveal that mechanisms contributing to the effects of arousal on sensory representations include state-dependent modulation of membrane potential dynamics, spontaneous firing, and tone-evoked synaptic potentials. Surprisingly, changes in short-latency tone-evoked excitatory input cannot explain the effects of arousal on the broadness of frequency-tuned output. However, we show that arousal strongly modulates a slow tone-evoked suppression of recurrent excitation underlying lateral inhibition [H. K. Kato, S. K. Asinof, J. S. Isaacson, Neuron, 95, 412–423, (2017)]. This arousal-dependent “network suppression” gates the duration of tone-evoked responses and regulates the broadness of frequency tuning. Thus, arousal can shape tuning via modulation of indirect changes in recurrent network activity.

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