Nominally non-responsive frontal and sensory cortical cells encode task-relevant variables via ensemble consensus-building

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These nominally non-responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here we describe a trial-by-trial, spike-timing-based algorithm to reveal the hidden coding capacities of these neurons in auditory and frontal cortex of behaving rats. Responsive and nominally non-responsive cells contained significant information about sensory stimuli and behavioral decisions, and network modeling indicated that nominally non-responsive cells are important for task performance. Sensory input was more accurately represented in frontal cortex than auditory cortex, via ensembles of nominally non-responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons - particularly those without obvious task-modulation - to be assessed for behavioral relevance on single trials.

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Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons

eLife ◽

10.7554/elife.42409 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 9

Author(s):

Michele N Insanally ◽

Ioana Carcea ◽

Rachel E Field ◽

Chris C Rodgers ◽

Brian DePasquale ◽

...

Keyword(s):

Frontal Cortex ◽

Cortical Neurons ◽

Sensory Input ◽

Spike Timing ◽

Stimulus Category ◽

Significant Information ◽

Sensory Stimuli ◽

Rate Modulation ◽

Ensemble Encoding ◽

And Behavior

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here, we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons – particularly those without obvious task-related, trial-averaged firing rate modulation – to be assessed for behavioral relevance on single trials.

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Sequence memory in recurrent neuronal network can develop without structured input

10.1101/2020.09.15.297580 ◽

2020 ◽

Author(s):

Matthias Loidolt ◽

Lucas Rudelt ◽

Viola Priesemann

Keyword(s):

Sensory Input ◽

Building Blocks ◽

Spike Timing ◽

Synaptic Competition ◽

Structured Input ◽

Recurrent Neuronal Network ◽

Sequence Memory ◽

The Brain ◽

Specific Sequences ◽

Intrinsic Feature

AbstractHow does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks “pre-shaped” by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa, but instead harness building blocks already present in the brain.

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Perinatal fentanyl exposure leads to long-lasting impairments in somatosensory circuit function and behavior

10.1101/2020.09.19.304352 ◽

2020 ◽

Author(s):

Jason Alipio ◽

Catherine Haga ◽

Megan E Fox ◽

Keiko Arakawa ◽

Rakshita Balaji ◽

...

Keyword(s):

Cortical Neurons ◽

Morphological Changes ◽

Anterior Cingulate ◽

Preclinical Model ◽

Sensory Stimuli ◽

Synaptic Excitation ◽

Molecular Abnormalities ◽

Postsynaptic Currents ◽

And Behavior

One consequence of the opioid epidemic are lasting neurodevelopmental sequelae afflicting adolescents exposed to opioids in the womb. A translationally relevant and developmentally accurate preclinical model is needed to understand the behavioral, circuit, network, and molecular abnormalities resulting from this exposure. By employing a novel preclinical model of perinatal fentanyl exposure, our data reveal that fentanyl has several dose-dependent, developmental consequences to somatosensory function and behavior. Newborn male and female mice exhibit signs of withdrawal and sensory-related deficits that extend at least to adolescence. As fentanyl exposure does not affect dams' health or maternal behavior, these effects result from the direct actions of perinatal fentanyl on the pups' developing brain. At adolescence, exposed mice exhibit reduced adaptation to sensory stimuli, and a corresponding impairment in primary somatosensory (S1) function. In vitro electrophysiology demonstrates a long-lasting reduction in S1 synaptic excitation, evidenced by decreases in release probability, NMDA receptor-mediated postsynaptic currents, and frequency of miniature excitatory postsynaptic currents, as well as increased frequency of miniature inhibitory postsynaptic currents. In contrast, anterior cingulate cortical neurons exhibit an opposite phenotype, with increased synaptic excitation. Consistent with these changes, electrocorticograms reveal suppressed ketamine-evoked γ oscillations. Morphological analysis of S1 pyramidal neurons indicate reduced dendritic complexity, dendritic length, and soma size. Further, exposed mice exhibited abnormal cortical mRNA expression of key receptors and neuronal growth and development, changes that were consistent with the electrophysiological and morphological changes. These findings demonstrate the lasting sequelae of perinatal fentanyl exposure on sensory processing and function.

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Fine Spike Timing in Hippocampal–Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains

Cerebral Cortex ◽

10.1093/cercor/bhaa216 ◽

2020 ◽

Vol 31 (1) ◽

pp. 147-158

Author(s):

Amanda E Hernan ◽

J Matthew Mahoney ◽

Willie Curry ◽

Seamus Mawe ◽

Rod C Scott

Keyword(s):

Working Memory ◽

Spatial Information ◽

Spatial Working Memory ◽

Spike Timing ◽

Functional Network ◽

Long Term Memory ◽

Encode Task ◽

To Receive ◽

Task Parameters

Abstract Spatial working memory (SWM) is a central cognitive process during which the hippocampus and prefrontal cortex (PFC) encode and maintain spatial information for subsequent decision-making. This occurs in the context of ongoing computations relating to spatial position, recall of long-term memory, attention, among many others. To establish how intermittently presented information is integrated with ongoing computations we recorded single units, simultaneously in hippocampus and PFC, in control rats and those with a brain malformation during performance of an SWM task. Neurons that encode intermittent task parameters are also well modulated in time and incorporated into a functional network across regions. Neurons from animals with cortical malformation are poorly modulated in time, less likely to encode task parameters, and less likely to be integrated into a functional network. Our results implicate a model in which ongoing oscillatory coordination among neurons in the hippocampal–PFC network describes a functional network that is poised to receive sensory inputs that are then integrated and multiplexed as working memory. The background temporal modulation is systematically altered in disease, but the relationship between these dynamics and behaviorally relevant firing is maintained, thereby providing potential targets for stimulation-based therapies.

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Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions

Neural Computation ◽

10.1162/neco_a_00520 ◽

2013 ◽

Vol 25 (12) ◽

pp. 3113-3130 ◽

Cited By ~ 8

Author(s):

Jan-Moritz P. Franosch ◽

Sebastian Urban ◽

J. Leo van Hemmen

Keyword(s):

Function Approximation ◽

Sensory Input ◽

Learning Rule ◽

Animal Learn ◽

Spike Timing ◽

Time Dependent ◽

Stable Configuration ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Tactile System

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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Frontal Cortex Neuron Type Classification with Deep Learning and Recurrence Plot

Traitement du signal ◽

10.18280/ts.380327 ◽

2021 ◽

Vol 38 (3) ◽

pp. 807-819

Author(s):

Fatma Özcan ◽

Ahmet Alkan

Keyword(s):

Deep Learning ◽

Frontal Cortex ◽

Recurrence Plot ◽

Image Features ◽

Spike Timing ◽

Nerve Cells ◽

Network Activity ◽

Cell Type ◽

Spike Sequences ◽

Type Classification

One of the goals of neural decoding in neuroscience is to create Brain-Computer Interfaces (BCI) that use nerve signals. In this context, we are interested in the activity of nerve cells. It is possible to classify nerve cells as excitatory or inhibitors by evaluating individual extra-cellular measurements taken from the frontal cortex of rats. Classification of neurons with only spike timing values has not been studied before, with deep learning, without knowing all of the wave properties and the intercellular interactions. In this study, inter-spike interval values of individual neuronal spike sequences were converted into recurrence plot images to analyze as point processing, image features were extracted using the pre-trained AlexNet with CNN deep learning method, and frontal cortex nerve cell type classification was made. Kernel classification, SVM, Naive Bayes, Ensemble, decision trees classification methods were used. The accuracy, sensitivity and specificity evaluate the proposed methods. A success of more than 81% has been achieved. Thus, the cell type is defined automatically. It has been observed that the ISI properties of spike trains can carry out information on cell type and thus neural network activity. Under these circumstances, these values are significant and important for neuroscientists.

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Phasic Stimuli Evoke Precisely Timed Spikes in Intermittently Discharging Mitral Cells

Journal of Neurophysiology ◽

10.1152/jn.00016.2004 ◽

2004 ◽

Vol 92 (2) ◽

pp. 743-753 ◽

Cited By ~ 76

Author(s):

Ramani Balu ◽

Phillip Larimer ◽

Ben W. Strowbridge

Keyword(s):

Olfactory Bulb ◽

Action Potentials ◽

Sensory Stimulation ◽

Spike Timing ◽

Mitral Cells ◽

Potential Oscillations ◽

Sensory Stimuli ◽

Subthreshold Oscillations ◽

Simple Step

Mitral cells, the principal cells of the olfactory bulb, respond to sensory stimulation with precisely timed patterns of action potentials. By contrast, the same neurons generate intermittent spike clusters with variable timing in response to simple step depolarizations. We made whole cell recordings from mitral cells in rat olfactory bulb slices to examine the mechanisms by which normal sensory stimuli could generate precisely timed spike clusters. We found that individual mitral cells fired clusters of action potentials at 20-40 Hz, interspersed with periods of subthreshold membrane potential oscillations in response to depolarizing current steps. TTX (1 μM) blocked a sustained depolarizing current and fast subthreshold oscillations in mitral cells. Phasic stimuli that mimic trains of slow excitatory postsynaptic potentials (EPSPs) that occur during sniffing evoked precisely timed spike clusters in repeated trials. The amplitude of the first simulated EPSP in a train gated the generation of spikes on subsequent EPSPs. 4-aminopyridine (4-AP)–sensitive K+ channels are critical to the generation of spike clusters and reproducible spike timing in response to phasic stimuli. Based on these results, we propose that spike clustering is a process that depends on the interaction between a 4-AP–sensitive K+ current and a subthreshold TTX-sensitive Na+ current; interactions between these currents may allow mitral cells to respond selectively to stimuli in the theta frequency range. These intrinsic properties of mitral cells may be important for precisely timing spikes evoked by phasic stimuli that occur in response to odor presentation in vivo.

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Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

Sustainability ◽

10.3390/su11236570 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6570 ◽

Cited By ~ 6

Author(s):

Hyun-Jeong Ban ◽

Hayeon Choi ◽

Eun-Kyong Choi ◽

Sanghyeop Lee ◽

Hak-Seon Kim

Keyword(s):

Factor Analysis ◽

Purchase Intention ◽

Semantic Network ◽

Information Source ◽

Online Review ◽

Significant Information ◽

Five Factors ◽

Food And Beverage ◽

And Behavior ◽

Relationship Of

With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers’ purchase intention and behavior. Therefore, it is important to understand the customer’s experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as “Intangible Service”, “Physical Environment”, “Purpose”, and “Location”. The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are “Access”, “F&B (Food and Beverage)”, “Purpose”, “Tangibles”, and “Empathy”. Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested.

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