scholarly journals Impact of precisely-timed inhibition of gustatory cortex on taste behavior depends on single-trial ensemble dynamics

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Narendra Mukherjee ◽  
Joseph Wachutka ◽  
Donald B Katz
Keyword(s):  
Firing Rate ◽  
Network Model ◽  
Motor Behavior ◽  
Single Trial ◽  
Gustatory Cortex ◽  
Attractor Network ◽  
Population Analyses ◽  
Stimulus Perception ◽  
Taste Processing

Sensation and action are necessarily coupled during stimulus perception – while tasting, for instance, perception happens while an animal decides to expel or swallow the substance in the mouth (the former via a behavior known as ‘gaping’). Taste responses in the rodent gustatory cortex (GC) span this sensorimotor divide, progressing through firing-rate epochs that culminate in the emergence of action-related firing. Population analyses reveal this emergence to be a sudden, coherent and variably-timed ensemble transition that reliably precedes gaping onset by 0.2–0.3s. Here, we tested whether this transition drives gaping, by delivering 0.5s GC perturbations in tasting trials. Perturbations significantly delayed gaping, but only when they preceded the action-related transition - thus, the same perturbation impacted behavior or not, depending on the transition latency in that particular trial. Our results suggest a distributed attractor network model of taste processing, and a dynamical role for cortex in driving motor behavior.

scholarly journals Dynamical structure of cortical taste responses revealed by precisely-timed optogenetic perturbation

2018 ◽  
Author(s):  
Narendra Mukherjee ◽  
Joseph Wachukta ◽  
Donald B Katz
Keyword(s):  
Network Model ◽  
Motor Behavior ◽  
Dynamical Structure ◽  
Gustatory Cortex ◽  
Attractor Network ◽  
Time Points ◽  
Population Analyses ◽  
Taste Processing

AbstractThe purpose of perception is driving action. During tasting, for instance, every stimulus must be either swallowed or rejected (the latter via a behavior known as “gaping”). Taste responses in the rodent primary gustatory cortex (GC) span this sensorimotor divide, progressing through a series of firing epochs that culminate in the emergence of action-related firing. Population analyses reveal this emergence to be a sudden, coherent ensemble transition that, despite varying in latency between trials, precedes gaping onset by 0.2-0.3s. Here, we tested whether this transition drives gaping, delivering 0.5s GC perturbations at various time-points in tasting trials. Perturbations significantly delayed gaping, but only when they preceded the action-related transition - thus, the same perturbation might have an impact or not, depending on the transition latency in that particular trial. Our results suggest a distributed attractor network model of taste processing, and a dynamical role for cortex in driving motor behavior.

scholarly journals Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

2017 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy G. Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Firing Rate ◽  
Spike Train ◽  
Prior Distribution ◽  
Kernel Smoothing ◽  
Neuronal Firing ◽  
Gaussian Kernel ◽  
Single Trial ◽  
Adaptive Kernel ◽  
Neuronal Firing Rate ◽  
Spike Train Data

AbstractNeurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

scholarly journals Cortical processing of chemosensory and hedonic features of taste in active licking mice

2020 ◽  
Author(s):  
Cecilia Bouaichi ◽  
Roberto Vincis
Keyword(s):  
Fixed Ratio ◽  
Stereotyped Behavior ◽  
Neural Dynamics ◽  
Ratio Schedule ◽  
Neural Processing ◽  
Fixed Ratio Schedule ◽  
Gustatory Cortex ◽  
Electrophysiological Recordings ◽  
Taste Processing ◽  
To Receive

ABSTRACTIn the last two decades, a considerable amount of work has been devoted to investigating the neural processing and dynamics of the primary taste cortex of rats. Surprisingly, much less information is available on cortical taste electrophysiology in awake mice, an animal model that is taking a more prominent role in taste research. Here we present electrophysiological evidence demonstrating how the gustatory cortex (GC) encodes information pertaining the basic taste qualities (sweet, salty, sour, and bitter) when stimuli are actively sampled through licking, the stereotyped behavior by which mice control the access of fluids in the mouth. Mice were trained to receive each stimulus on a fixed ratio schedule in which they had to lick a dry spout six times to receive a tastant on the seventh lick. Electrophysiological recordings confirmed that GC neurons encode both chemosensory and hedonic aspects of actively sampled tastants. In addition, our data revealed two other main findings; GC neurons encoded information about taste identity in as little as 120 ms. Consistent with the ability of GC neurons to rapidly encode taste information, nearly half of the recorded neurons exhibited spiking activity that was entrained to licking at rates up to 8 Hz. Overall, our results highlight how the GC of mice processes tastants when they are actively sensed through licking, reaffirming and expanding our knowledge on cortical taste processing.NEW & NOTEWORTHYRelatively little information is available on the neural dynamics of taste processing in the mouse gustatory cortex (GC). In this study we investigate how the GC encodes information of the qualities and hedonics of a broad panel of gustatory stimuli when tastants are actively sampled through licking. Our results show that the GC neurons broadly encode taste qualities but also process taste hedonics and licking information in a temporally dynamic manner.

Temporal Sparseness of the Premotor Drive Is Important for Rapid Learning in a Neural Network Model of Birdsong

2004 ◽  
Vol 92 (4) ◽  
pp. 2274-2282 ◽  
Author(s):  
Ila R. Fiete ◽  
Richard H.R. Hahnloser ◽  
Michale S. Fee ◽  
H. Sebastian Seung
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Motor Neurons ◽  
Neural Network Model ◽  
Motor Behavior ◽  
Analytical Techniques ◽  
Temporal Coding ◽  
Neural Codes ◽  
Learning Time ◽  
Complex Motor

Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.

scholarly journals Cortical processing of chemosensory and hedonic features of taste in active licking mice

2020 ◽  
Vol 123 (5) ◽  
pp. 1995-2009 ◽  
Author(s):  
Cecilia G. Bouaichi ◽  
Roberto Vincis
Keyword(s):  
Neural Dynamics ◽  
Cortical Processing ◽  
Gustatory Cortex ◽  
Basic Taste ◽  
Taste Processing

Relatively little information is available on the neural dynamics of taste processing in the mouse gustatory cortex (GC). In this study we investigate how the GC encodes chemosensory and palatability features of a wide panel of gustatory stimuli when actively sampled through licking. Our results show that GC neurons broadly encode basic taste qualities but also process taste hedonics and licking information in a temporally dynamic manner.

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