1
|
Nesse WH, Clark KL, Noudoost B. Information representation in an oscillating neural field model modulated by working memory signals. Front Comput Neurosci 2024; 17:1253234. [PMID: 38303900 PMCID: PMC10830742 DOI: 10.3389/fncom.2023.1253234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/27/2023] [Indexed: 02/03/2024] Open
Abstract
We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus feature-on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation-the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.
Collapse
Affiliation(s)
- William H. Nesse
- Department of Mathematics, University of Utah, Salt Lake City, UT, United States
| | - Kelsey L. Clark
- Department of Ophthalmology, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology, University of Utah, Salt Lake City, UT, United States
| |
Collapse
|
2
|
Comeaux P, Clark K, Noudoost B. A recruitment through coherence theory of working memory. Prog Neurobiol 2023; 228:102491. [PMID: 37393039 PMCID: PMC10530428 DOI: 10.1016/j.pneurobio.2023.102491] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
The interactions between prefrontal cortex and other areas during working memory have been studied for decades. Here we outline a conceptual framework describing interactions between these areas during working memory, and review evidence for key elements of this model. We specifically suggest that a top-down signal sent from prefrontal to sensory areas drives oscillations in these areas. Spike timing within sensory areas becomes locked to these working-memory-driven oscillations, and the phase of spiking conveys information about the representation available within these areas. Downstream areas receiving these phase-locked spikes from sensory areas can recover this information via a combination of coherent oscillations and gating of input efficacy based on the phase of their local oscillations. Although the conceptual framework is based on prefrontal interactions with sensory areas during working memory, we also discuss the broader implications of this framework for flexible communication between brain areas in general.
Collapse
Affiliation(s)
- Phillip Comeaux
- Dept. of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Salt Lake City, UT 84112, USA; Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Kelsey Clark
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Behrad Noudoost
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA.
| |
Collapse
|
3
|
Vivekanandhan G, Mehrabbeik M, Rajagopal K, Jafari S, Lomber SG, Merrikhi Y. Higuchi fractal dimension is a unique indicator of working memory content represented in spiking activity of visual neurons in extrastriate cortex. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3749-3767. [PMID: 36899603 DOI: 10.3934/mbe.2023176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Working memory has been identified as a top-down modulation of the average spiking activity in different brain parts. However, such modification has not yet been reported in the middle temporal (MT) cortex. A recent study showed that the dimensionality of the spiking activity of MT neurons increases after deployment of spatial working memory. This study is devoted to analyzing the ability of nonlinear and classical features to capture the content of the working memory from the spiking activity of MT neurons. The results suggest that only the Higuchi fractal dimension can be considered as a unique indicator of working memory while the Margaos-Sun fractal dimension, Shannon entropy, corrected conditional entropy, and skewness are perhaps indicators of other cognitive factors such as vigilance, awareness, and arousal as well as working memory.
Collapse
Affiliation(s)
| | - Mahtab Mehrabbeik
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, India
- Department of Electronics and Communications Engineering and University Centre of Research & Development, Chandigarh University, Mohali 140413, Punjab
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Stephen G Lomber
- Department of Physiology, Faculty of Medicine, McGill University, Montreal H3G 1Y6, Canada
| | - Yaser Merrikhi
- Department of Physiology, Faculty of Medicine, McGill University, Montreal H3G 1Y6, Canada
| |
Collapse
|
4
|
Vivekanandhan G, Mehrabbeik M, Rajagopal K, Jafari S, Lomber SG, Merrikhi Y. Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3216-3236. [PMID: 36899578 DOI: 10.3934/mbe.2023151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Neural signatures of working memory have been frequently identified in the spiking activity of different brain areas. However, some studies reported no memory-related change in the spiking activity of the middle temporal (MT) area in the visual cortex. However, recently it was shown that the content of working memory is reflected as an increase in the dimensionality of the average spiking activity of the MT neurons. This study aimed to find the features that can reveal memory-related changes with the help of machine-learning algorithms. In this regard, different linear and nonlinear features were obtained from the neuronal spiking activity during the presence and absence of working memory. To select the optimum features, the Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization methods were employed. The classification was performed using the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers. Our results suggest that the deployment of spatial working memory can be perfectly detected from spiking patterns of MT neurons with an accuracy of 99.65±0.12 using the KNN and 99.50±0.26 using the SVM classifiers.
Collapse
Affiliation(s)
| | - Mahtab Mehrabbeik
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, India
- Department of Electronics and Communications Engineering and University Centre of Research & Development, Chandigarh University, Mohali 140413, Punjab
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Stephen G Lomber
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, H3G 1Y6, Canada
| | - Yaser Merrikhi
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, H3G 1Y6, Canada
| |
Collapse
|
5
|
Zarei M, Jahed M, Dezfouli MP, Daliri MR. Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times. Brain Struct Funct 2022; 227:1641-1654. [PMID: 35106628 DOI: 10.1007/s00429-022-02460-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022]
Abstract
Neural synchronization has been engaged in several brain mechanisms. Previous studies have shown that the interaction between the time of spiking activity and phase of local field potentials (LFPs) plays a key role in many cognitive functions. However, the potential role of this spike-LFP phase coupling (SPC) in neural coding is not fully understood. Here, we sought to investigate the role of this SPC for encoding the sensory properties of visual stimuli. To this end, we measured SPC strength in the preferred and anti-preferred motion directions of stimulus presented inside the receptive field of middle temporal (MT) neurons. We found a selective response in terms of SPC strength for different directions of motion. Remarkably, this selective function is inverted with respect to the spiking activity. In other words, the least SPC occurs for the preferred direction (based on the spike rate), and vice versa; the strongest SPC is induced in the anti-preferred direction. Altogether, these findings imply that the neural system may use spike-LFP phase coupling in the primate visual cortex to encode sensory information.
Collapse
Affiliation(s)
- Mohammad Zarei
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,School of Electrical Engineering, Sharif University of Technology (SUT), Tehran, Iran
| | - Mehran Jahed
- School of Electrical Engineering, Sharif University of Technology (SUT), Tehran, Iran.
| | - Mohsen Parto Dezfouli
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Reza Daliri
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| |
Collapse
|
6
|
Ding J, Ye Z, Xu F, Hu X, Yu H, Zhang S, Tu Y, Zhang Q, Sun Q, Hua T, Lu ZL. Effects of top-down influence suppression on behavioral and V1 neuronal contrast sensitivity functions in cats. iScience 2022; 25:103683. [PMID: 35059603 PMCID: PMC8760559 DOI: 10.1016/j.isci.2021.103683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/16/2021] [Accepted: 12/20/2021] [Indexed: 02/09/2023] Open
Abstract
To explore the relative contributions of higher-order and primary visual cortex (V1) to visual perception, we compared cats' behavioral and V1 neuronal contrast sensitivity functions (CSF) and threshold versus external noise contrast (TvC) functions before and after top-down influence of area 7 (A7) was modulated with transcranial direct current stimulation (tDCS). We found that suppressing top-down influence of A7 with cathode-tDCS, but not sham-tDCS, reduced behavioral and neuronal contrast sensitivity in the same range of spatial frequencies and increased behavioral and neuronal contrast thresholds in the same range of external noise levels. The neuronal CSF and TvC functions were highly correlated with their behavioral counterparts both before and after the top-down suppression. Analysis of TvC functions using the Perceptual Template Model (PTM) indicated that top-down influence of A7 increased both behavioral and V1 neuronal contrast sensitivity by reducing internal additive noise and the impact of external noise. Top-down suppression lowers both behavioral and V1 neuronal CSF functions Top-down suppression raises both behavioral and V1 neuronal TvC functions The neuronal CSFs and TvCs are highly correlated with their behavioral counterparts Top-down influence lowers internal additive noise and impact of external noise in V1
Collapse
Affiliation(s)
- Jian Ding
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Zheng Ye
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Fei Xu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Xiangmei Hu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Hao Yu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Shen Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Yanni Tu
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Qiuyu Zhang
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Qingyan Sun
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Tianmiao Hua
- College of Life Sciences, Anhui Normal University, Wuhu, Anhui 241000, China
| | - Zhong-Lin Lu
- Divison of Arts and Sciences, NYU Shanghai, Shanghai 200122, China.,Center for Neural Science and Department of Psychology, New York University, New York, NY 10003, USA.,NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai 200062, China
| |
Collapse
|
7
|
Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
Collapse
Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| |
Collapse
|
8
|
Davoudi S, Parto Dezfouli M, Knight RT, Daliri MR, Johnson EL. Prefrontal Lesions Disrupt Posterior Alpha-Gamma Coordination of Visual Working Memory Representations. J Cogn Neurosci 2021; 33:1798-1810. [PMID: 34375418 DOI: 10.1162/jocn_a_01715] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of "where" and "when" features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8-12 Hz) oscillations and topographically distributed gamma (γ; 30-50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α-γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α-γ mechanism for the rapid selection of visual WM representations.
Collapse
Affiliation(s)
- Saeideh Davoudi
- University of Montréal, Quebec, Canada.,CHU Sainte-Justine Research Center, Montréal, Quebec, Canada.,Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohsen Parto Dezfouli
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.,School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Mohammad Reza Daliri
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.,School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Elizabeth L Johnson
- University of California, Berkeley.,Wayne State University, Detroit, Michigan
| |
Collapse
|
9
|
Nesse WH, Bahmani Z, Clark K, Noudoost B. Differential Contributions of Inhibitory Subnetwork to Visual Cortical Modulations Identified via Computational Model of Working Memory. Front Comput Neurosci 2021; 15:632730. [PMID: 34093155 PMCID: PMC8173146 DOI: 10.3389/fncom.2021.632730] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 04/13/2021] [Indexed: 11/30/2022] Open
Abstract
Extrastriate visual neurons show no firing rate change during a working memory (WM) task in the absence of sensory input, but both αβ oscillations and spike phase locking are enhanced, as is the gain of sensory responses. This lack of change in firing rate is at odds with many models of WM, or attentional modulation of sensory networks. In this article we devised a computational model in which this constellation of results can be accounted for via selective activation of inhibitory subnetworks by a top-down working memory signal. We confirmed the model prediction of selective inhibitory activation by segmenting cells in the experimental neural data into putative excitatory and inhibitory cells. We further found that this inhibitory activation plays a dual role in influencing excitatory cells: it both modulates the inhibitory tone of the network, which underlies the enhanced sensory gain, and also produces strong spike-phase entrainment to emergent network oscillations. Using a phase oscillator model we were able to show that inhibitory tone is principally modulated through inhibitory network gain saturation, while the phase-dependent efficacy of inhibitory currents drives the phase locking modulation. The dual contributions of the inhibitory subnetwork to oscillatory and non-oscillatory modulations of neural activity provides two distinct ways for WM to recruit sensory areas, and has relevance to theories of cortical communication.
Collapse
Affiliation(s)
- William H Nesse
- Department of Mathematics, University of Utah, Salt Lake City, UT, United States
| | - Zahra Bahmani
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Kelsey Clark
- Department of Ophthalmology, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology, University of Utah, Salt Lake City, UT, United States
| |
Collapse
|
10
|
Zarei M, Parto Dezfouli M, Jahed M, Daliri MR. Adaptation Modulates Spike-Phase Coupling Tuning Curve in the Rat Primary Auditory Cortex. Front Syst Neurosci 2020; 14:55. [PMID: 32848646 PMCID: PMC7416672 DOI: 10.3389/fnsys.2020.00055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 07/13/2020] [Indexed: 12/02/2022] Open
Abstract
Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes’ times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.
Collapse
Affiliation(s)
- Mohammad Zarei
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,School of Electrical Engineering, Sharif University of Technology (SUT), Tehran, Iran
| | - Mohsen Parto Dezfouli
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Neuroscience and Neuroengineering Research Laboratory, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mehran Jahed
- School of Electrical Engineering, Sharif University of Technology (SUT), Tehran, Iran
| | - Mohammad Reza Daliri
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Neuroscience and Neuroengineering Research Laboratory, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| |
Collapse
|
11
|
Hofmann V, Chacron MJ. Novel Functions of Feedback in Electrosensory Processing. Front Integr Neurosci 2019; 13:52. [PMID: 31572137 PMCID: PMC6753188 DOI: 10.3389/fnint.2019.00052] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 08/26/2019] [Indexed: 11/13/2022] Open
Abstract
Environmental signals act as input and are processed across successive stages in the brain to generate a meaningful behavioral output. However, a ubiquitous observation is that descending feedback projections from more central to more peripheral brain areas vastly outnumber ascending feedforward projections. Such projections generally act to modify how sensory neurons respond to afferent signals. Recent studies in the electrosensory system of weakly electric fish have revealed novel functions for feedback pathways in that their transformation of the afferent input generates neural firing rate responses to sensory signals mediating perception and behavior. In this review, we focus on summarizing these novel and recently uncovered functions and put them into context by describing the more "classical" functions of feedback in the electrosensory system. We further highlight the parallels between the electrosensory system and other systems as well as outline interesting future directions.
Collapse
Affiliation(s)
- Volker Hofmann
- Department of Physiology, McGill University, Montreal, QC, Canada
| | | |
Collapse
|