Mohammad Reza Daliri - Academia.edu (original) (raw)

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Papers by Mohammad Reza Daliri

Frontiers in Systems Neuroscience

Adaptation is an important mechanism that causes a decrease in the neural response both in terms ... more 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.

2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

Electroencephalography (EEG), as the most common tool for epileptic seizure classification, conta... more Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time-frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups' patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.

BMC Bioinformatics

Background Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a con... more Background Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal. Results The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ($$R^{2}$$ R 2 ) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was a...

PLOS Biology

Attending to visual stimuli enhances the gain of those neurons in primate visual cortex that pref... more Attending to visual stimuli enhances the gain of those neurons in primate visual cortex that preferentially respond to the matching locations and features (on-target gain). Although this is well suited to enhance the neuronal representation of attended stimuli, it is nonoptimal under difficult discrimination conditions, as in the presence of similar distractors. In such cases, directing attention to neighboring neuronal populations (off-target gain) has been shown to be the most efficient strategy, but although such a strategic deployment of attention has been shown behaviorally, its underlying neural mechanisms are unknown. Here, we investigated how attention affects the population responses of neurons in the middle temporal (MT) visual area of rhesus monkeys to bidirectional movement inside the neurons' receptive field (RF). The monkeys were trained to focus their attention onto the fixation spot or to detect a direction or speed change in one of the motion directions (the "target"), ignoring the distractor motion. Population activity profiles were determined by systematically varying the patterns' directions while maintaining a constant angle between them. As expected, the response profiles show a peak for each of the 2 motion directions. Switching spatial attention from the fixation spot into the RF enhanced the peak representing the attended stimulus and suppressed the distractor representation. Importantly, the population data show a direction-dependent attentional modulation that does not peak at the target feature but rather along the slopes of the activity profile representing the target direction. Our results show that attentional gains are strategically deployed to optimize the discriminability of target stimuli, in line with an optimal gain mechanism proposed by Navalpakkam and Itti.

Attention selectively routes the most behaviorally relevant information among the vast pool of se... more Attention selectively routes the most behaviorally relevant information among the vast pool of sensory inputs through cortical regions. Previous studies have shown that visual attention samples the surrounding stimuli periodically. However, the neural mechanism underlying this sampling in the sensory cortex, and whether the brain actively uses these rhythms, has remained elusive. Here, we hypothesize that selective attention controls the phase of oscillatory synaptic activities to efficiently process the relevant information in the brain. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) at low frequencies in macaque visual area MT. Our data reveal that phase coherence increases when attention is deployed towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the attentional modulatio...

Behavioural Brain Research

Neural Computing and Applications

Frontiers in Neuroscience

The idea that a flexible behavior relies on synchronous neural activity within intra-and inter-as... more The idea that a flexible behavior relies on synchronous neural activity within intra-and inter-associated cortical areas has been a matter of intense research in human and animal neuroscience. The neurophysiological mechanisms underlying this behavioral correlate of the synchronous activity are still unknown. It has been suggested that the strength of neural synchrony at the level of population is an important neural code to guide an efficient transformation of the sensory input into the behavioral action. In this study, we have examined the non-linear synchronization between neural ensembles in area MT of the macaque visual cortex by employing a non-linear cross-frequency coupling technique, namely bicoherence. We trained a macaque monkey to detect a brief change in the cued stimulus during a visuomotor detection task. The results show that the non-linear phase synchronization in the high-gamma frequency band (100-250 Hz) predicts the animal's reaction time. The strength of non-linear phase synchronization is selective to the target stimulus location. In addition, the non-linearity characteristics of neural synchronization are selectively modulated when the monkey covertly attends to the stimulus inside the neuron's receptive field. This additional evidence indicates that non-linear neuronal synchronization may be affected by a cognitive function like spatial attention. Our neural and behavioral observations reflect that two crucial processes may be involved in processing of visuomotor information in area MT: (I) a non-linear cortical process (measured by the bicoherence) and (II) a linear process (measured by the spectral power).

Scientific Reports

Attention selectively routes the most behaviorally relevant information from the stream of sensor... more Attention selectively routes the most behaviorally relevant information from the stream of sensory inputs through the hierarchy of cortical areas. Previous studies have shown that visual attention depends on the phase of oscillatory brain activities. These studies mainly focused on the stimulus presentation period, rather than the pre-stimulus period. Here, we hypothesize that selective attention controls the phase of oscillatory neural activities to efficiently process relevant information. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) of low frequency local field potentials (LFP) in visual area MT of macaque monkeys. Our data reveal that phase coherence increases following a spatial cue deploying attention towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the modulation of ...

Frontiers in Behavioral Neuroscience

How neural activity is linked to behavior is a critical question in neural engineering and cognit... more How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit's speed in information processing.

Journal of Neural Engineering

Journal of Bionic Engineering

Signal, Image and Video Processing

Australasian Physical & Engineering Sciences in Medicine

BMC Biology

Background: The timing of action potentials ("spikes") of cortical neurons has been shown to be a... more Background: The timing of action potentials ("spikes") of cortical neurons has been shown to be aligned to the phase of low-frequency (< 10 Hz) local field potentials (LFPs) in several cortical areas. However, across the areas, this alignment varies and the role of this spike-phase coupling (SPC) in cognitive functions is not well understood. Results: Here, we propose a role in the coordination of neural activity by selective attention. After refining previous analytical methods for measuring SPC, we show that first, SPC is present along the dorsal processing pathway in macaque visual cortex (area MT); second, spikes occur in falling phases of the low-frequency LFP independent of the location of spatial attention; third, switching spatial attention into the receptive field (RF) of MT neurons decreases this coupling; and finally, the LFP phase causally influences the spikes. Conclusions: Here, we show that spikes are coupled to the phase of low-frequency LFP along the dorsal visual pathway. Our data suggest that attention harnesses this spike-LFP coupling to de-synchronize neurons and thereby enhance the neural representation of the attended stimuli.

Frontiers in Neural Circuits

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This ... more Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike-LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.