Stimulus-Dependent Assembly Formation of Oscillatory Responses: I. Synchronization

1991 ◽  
Vol 3 (2) ◽  
pp. 155-166 ◽  
Author(s):  
Peter König ◽  
Thomas B. Schillen
Keyword(s):  
Nonlinear Oscillator ◽  
Experimental Situation ◽  
Temporal Correlation ◽  
Temporal Coding ◽  
Two Dimensional ◽  
Neuronal Populations ◽  
Cortical Areas ◽  
Short Stimulus ◽  
Object Features ◽  
The Brain

Current concepts in neurobiology of vision assume that local object features are represented by distributed neuronal populations in the brain. Such representations can lead to ambiguities if several distinct objects are simultaneously present in the visual field. Temporal characteristics of the neuronal activity have been proposed as a possible solution to this problem and have been found in various cortical areas. In this paper we introduce a delayed nonlinear oscillator to investigate temporal coding in neuronal networks. We show synchronization within two-dimensional layers consisting of oscillatory elements coupled by excitatory delay connections. The observed correlation length is large compared to coupling length. Following the experimental situation, we then demonstrate the response of such layers to two short stimulus bars of varying gap distance. Coherency of stimuli is reflected by the temporal correlation of the responses, which closely resembles the experimental observations.

scholarly journals Formation of Category Representations in Superior Temporal Sulcus

2010 ◽  
Vol 22 (6) ◽  
pp. 1270-1282 ◽  
Author(s):  
Marieke van der Linden ◽  
Miranda van Turennout ◽  
Peter Indefrey
Keyword(s):  
Visual Experience ◽  
Temporal Cortex ◽  
Category Boundary ◽  
Superior Temporal Sulcus ◽  
Neuronal Populations ◽  
Cortical Areas ◽  
Object Categories ◽  
Highly Sensitive ◽  
Fmri Adaptation ◽  
The Brain

The human brain contains cortical areas specialized in representing object categories. Visual experience is known to change the responses in these category-selective areas of the brain. However, little is known about how category training specifically affects cortical category selectivity. Here, we investigated the experience-dependent formation of object categories using an fMRI adaptation paradigm. Outside the scanner, subjects were trained to categorize artificial bird types into arbitrary categories (jungle birds and desert birds). After training, neuronal populations in the occipito-temporal cortex, such as the fusiform and the lateral occipital gyrus, were highly sensitive to perceptual stimulus differences. This sensitivity was not present for novel birds, indicating experience-related changes in neuronal representations. Neurons in STS showed category selectivity. A release from adaptation in STS was only observed when two birds in a pair crossed the category boundary. This dissociation could not be explained by perceptual similarities because the physical difference between birds from the same side of the category boundary and between birds from opposite sides of the category boundary was equal. Together, the occipito-temporal cortex and the STS have the properties suitable for a system that can both generalize across stimuli and discriminate between them.

scholarly journals EEG Analysis during Music Perception

2020 ◽  
Author(s):  
González Almudena ◽  
Santapau Manuel ◽  
González Julián Jesús
Keyword(s):  
Time Domain ◽  
Music Perception ◽  
Spectral Power ◽  
Experimental Situation ◽  
Eeg Analysis ◽  
Cortical Areas ◽  
Musical Perception ◽  
Musical Characteristics ◽  
Frequency Domain Techniques ◽  
The Brain

This review presents the most interesting results of electroencephalographic studies on musical perception performed with different analysis techniques. In first place, concepts on intra-musical characteristics such as tonality, rhythm, dissonance or musical syntax, which have been object of further investigation, are introduced. Most of the studies found use listening musical extracts, sequences of notes or chords as an experimental situation, with the participants in a resting situation. There are few works with participants performing or imagining musical performance. The reviewed works have been divided into two groups: a) those that analyze the EEGs recorded in different cortical areas separately using frequency domain techniques: spectral power, phase or time domain EEG procedures such as potentials event related (ERP); b) those that investigate the interdependence between different EEG channels to evaluate the functional connectivity between different cortical areas through different statistical or synchronization indices. Most of the aspects studied in music-brain interaction are those related to musical emotions, syntax of different musical styles, musical expectation, differences between pleasant and unpleasant music and effects of musical familiarity and musical experience. Most of the works try to know the topographic maps of the brain centers, pathways and functions involved in these aspects.

Stimulus-Dependent Assembly Formation of Oscillatory Responses: II. Desynchronization

1991 ◽  
Vol 3 (2) ◽  
pp. 167-178 ◽  
Author(s):  
Thomas B. Schillen ◽  
Peter König
Keyword(s):  
Visual Cortex ◽  
Temporal Structure ◽  
Nonlinear Oscillators ◽  
Oscillatory Activity ◽  
Two Dimensional ◽  
Potential Mechanism ◽  
Neuronal Responses ◽  
Binding Problem ◽  
Cortical Areas ◽  
The Brain

Recent theoretical and experimental work suggests a temporal structure of neuronal spike activity as a potential mechanism for solving the binding problem in the brain. In particular, recordings from cat visual cortex demonstrate the possibility that stimulus coherency is coded by synchronization of oscillatory neuronal responses. Coding by synchronized oscillatory activity has to avoid bulk synchronization within entire cortical areas. Recent experimental evidence indicates that incoherent stimuli can activate coherently oscillating assemblies of cells that are not synchronized among one another. In this paper we show that appropriately designed excitatory delay connections can support the desynchronization of two-dimensional layers of delayed nonlinear oscillators. Closely following experimental observations, we then present two examples of stimulus-dependent assembly formation in oscillatory layers that employ both synchronizing and desynchronizing delay connections: First, we demonstrate the segregation of oscillatory responses to two overlapping but incoherently moving stimuli. Second, we show that the coherence of movement and location of two stimulus bar segments can be coded by the correlation of oscillatory activity.

Mesencephalic GABA neuronal development: no more on the other side of oblivion

2014 ◽  
Vol 5 (5) ◽  
pp. 371-382 ◽  
Author(s):  
Suyan Li ◽  
Sampada Joshee ◽  
Anju Vasudevan
Keyword(s):  
Transcriptional Control ◽  
Neuronal Development ◽  
Developmental Regulation ◽  
Molecular Characteristics ◽  
Psychiatric Illnesses ◽  
Neuronal Populations ◽  
Gaba Neurons ◽  
Recent Developments ◽  
And Function ◽  
The Brain

AbstractMidbrain GABA neurons, endowed with multiple morphological, physiological and molecular characteristics as well as projection patterns are key players interacting with diverse regions of the brain and capable of modulating several aspects of behavior. The diversity of these GABA neuronal populations based on their location and function in the dorsal, medial or ventral midbrain has challenged efforts to rapidly uncover their developmental regulation. Here we review recent developments that are beginning to illuminate transcriptional control of GABA neurons in the embryonic midbrain (mesencephalon) and discuss its implications for understanding and treatment of neurological and psychiatric illnesses.

scholarly journals Functional Connectivity within Brain Networks of Long Term and Short term Meditators

2019 ◽  
Vol 9 (2S) ◽  
pp. 29-34
Keyword(s):  
Functional Connectivity ◽  
Temporal Correlation ◽  
Brain Regions ◽  
Invasive Technique ◽  
Long Term Effects ◽  
Short Term ◽  
Statistical Concept ◽  
The Brain ◽  
Meditative Practices

Meditation refers to a state of mind of relaxation and concentration, where generally the mind and body is at rest. The process of meditation reflects the state of the brain which is distinct from sleep or typical wakeful states of consciousness. Meditative practices usually involve regulation of emotions and monitoring of attention. Over the past decade there has been a tremendous increase in an interest to study the neural mechanisms involved in meditative practices. It could also be beneficial to explore if the effect of meditation is altered by the number of years of meditation practice. Functional Magnetic Resonance Imaging (fMRI) is a very useful imaging technique which can be used to perform this analysis due to its inherent benefits, mainly it being a non-invasive technique. Functional activation and connectivity analysis can be performed on the fMRI data to find the active regions and the connectivity in the brain regions. Functional connectivity is defined as a simple temporal correlation between anatomically separate, active neural regions. Functional connectivity gives the statistical dependencies between regional time series. It is a statistical concept and is quantified using metrics like Correlation. In this study, a comparison is made between functional connectivity in the brain regions of long term meditation practitioners (LTP) and short-term meditation practitioners (STP) to see the differences and similarities in the connectivity patterns. From the analysis, it is evident that in fact there is a difference in connectivity between long term and short term practitioners and hence continuous practice of meditation can have long term effects.

Capacity of a Single Spiking Neuron Channel

2009 ◽  
Vol 21 (6) ◽  
pp. 1714-1748 ◽  
Author(s):  
Shiro Ikeda ◽  
Jonathan H. Manton
Keyword(s):  
Information Processing ◽  
Channel Capacity ◽  
Information Transfer ◽  
Communication Channel ◽  
Discrete Distribution ◽  
Random Variable ◽  
Temporal Coding ◽  
Reasonable Assumption ◽  
Rate Coding ◽  
The Brain

Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.

Hierarchical Dynamical Model for Multiple Cortical Neural Decoding

2021 ◽  
Vol 33 (5) ◽  
pp. 1372-1401
Author(s):  
Xi Liu ◽  
Xiang Shen ◽  
Shuhang Chen ◽  
Xiang Zhang ◽  
Yifan Huang ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Primary Motor Cortex ◽  
Neural Pathway ◽  
Neural Recordings ◽  
Cortical Areas ◽  
The Hierarchical Structure ◽  
Brain States ◽  
Heavy Tailed ◽  
The Brain

Abstract Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a prosthesis. As the subject adapts to control the neural prosthesis, the medial prefrontal cortex (mPFC), upstream of the primary motor cortex (M1), is heavily involved in reward-guided motor learning. Thus, considering mPFC and M1 functionality within a hierarchical structure could potentially improve the effectiveness of BMI decoding while subjects are learning. The commonly used Kalman decoding method with only one simple state model may not be able to represent the multiple brain states that evolve over time as well as along the neural pathway. In addition, the performance of Kalman decoders degenerates in heavy-tailed nongaussian noise, which is usually generated due to the nonlinear neural system or influences of movement-related noise in online neural recording. In this letter, we propose a hierarchical model to represent the brain states from multiple cortical areas that evolve along the neural pathway. We then introduce correntropy theory into the hierarchical structure to address the heavy-tailed noise existing in neural recordings. We test the proposed algorithm on in vivo recordings collected from the mPFC and M1 of two rats when the subjects were learning to perform a lever-pressing task. Compared with the classic Kalman filter, our results demonstrate better movement decoding performance due to the hierarchical structure that integrates the past failed trial information over multisite recording and the combination with correntropy criterion to deal with noisy heavy-tailed neural recordings.

Early specification and autonomous development of cortical fields in the mouse hippocampus

Development ◽  
1997 ◽  
Vol 124 (24) ◽  
pp. 4959-4970 ◽  
Author(s):  
S. Tole ◽  
C. Christian ◽  
E.A. Grove
Keyword(s):  
Gene Expression ◽  
Slice Culture ◽  
Field Development ◽  
Cortical Areas ◽  
Hippocampal Field ◽  
Mouse Hippocampus ◽  
Hippocampal Development ◽  
Autonomous Development ◽  
The Brain ◽  
Development Proceeds

Studies of the specification of distinct areas in the developing cerebral cortex have until now focused mainly on neocortex. We demonstrate that the hippocampus, an archicortical structure, offers an elegant, alternative system in which to explore cortical area specification. Individual hippocampal areas, called CA fields, display striking molecular differences in maturity. We use these distinct patterns of gene expression as markers of CA field identity, and show that the two major hippocampal fields, CA1 and CA3, are specified early in hippocampal development, during the period of neurogenesis. Two field-specific markers display consistent patterns of expression from the embryo to the adult. Presumptive CA1 and CA3 fields (Pca1, Pca3) can therefore be identified between embryonic days 14.5 and 15.5 in the mouse, a week before the fields are morphologically distinct. No other individual cortical areas have been detected by gene expression as early in development. Indeed, other features that distinguish between the CA fields appear after birth, indicating that mature CA field identity is acquired over at least 3 weeks. To determine if Pca1 and Pca3 are already specified to acquire mature CA field identities, the embryonic fields were isolated from further potential specification cues by maintaining them in slice culture. CA field development proceeds in slices of the entire embryonic hippocampus. More strikingly, slices restricted to Pca1 or Pca3 alone also develop appropriate mature features of CA1 or CA3. Pca1 and Pca3 are therefore able to develop complex characteristics of mature CA field identity autonomously, that is, without contact or innervation from other fields or other parts of the brain. Because Pca1 and Pca3 can be identified before major afferents grow into the hippocampus, innervation may also be unnecessary for the initial division of the hippocampus into separate fields. Providing a clue to the source of the true specifying signals, the earliest field markers appear first at the poles of the hippocampus, then progress inwards. General hippocampal development does not follow this pronounced pattern. We suggest that the sources of signals that specify hippocampal field identity lie close to the hippocampal poles, and that the signals operate first on cells at the poles, then move inwards.

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