147 research outputs found
Recommended from our members
Mirror touch: Electrophysiological and behavioural evidence on the effects of observing othersâ tactile sensations on somatosensory processing in the observer and possible links to trait empathy
Recent neuroimaging evidence suggests that the sight of somebody else being touched vicariously triggers activity in the secondary (SII) and possibly also the primary (SI) somatosensory cortex in the absence of any actual tactile stimulation on the onlookerâs own body. The present PhD thesis aimed to investigate electrophysiological and behavioural correlates of such shared neural representations for actually experienced and merely observed touch, importantly, not only in the context of observing somebody else being passively touched on their body but also in the context of witnessing somebody else perform actions with a tactile component (such as actively touching an object). In addition, the present thesis intended to explore possible links between variations in the strength of touch observation-related modulations in somatosensory processing and interindividual differences in dispositional empathy.
The obtained electrophysiological data indicated that, first of all, the sight of othersâ passive tactile sensations modulated somatosensory activity relatively consistently at both early and late processing stages within the first 200 ms after tactile stimulus onset. These modulations occurred independently of whether the touch target was actually a human body or merely an inanimate object (Exp. 2.1). The perspective from which a body part was observed to be touched did differentially affect touch observation-related ERP modulations, but only during later-stage somatosensory processing (Exp. 2.2). The electrophysiological evidence further suggested that while the somatotopic organisation of vicarious somatosensory activations might not be fine-grained enough to represent which location within a given body part was seen to be touched (Exp. 2.3), it might nevertheless be sufficiently detailed (at least in SI) to code the touched location at the level of different body parts (Exp. 2.4). The sight of othersâ action-embedded tactile sensations was, too, found to alter ongoing somatosensory activity but the pattern of modulations was rather complex and fragile (Exp.s 3.1-3.6), possibly in the context of movement observation-related vicarious somatosensory activity which might sometimes have obscured much subtler touch observation-related resonance responses, especially if participants were not sufficiently aware of the tactile component in the observed actions. Behaviourally, the sight of otherâs (active) touch sensations was nevertheless associated with systematic shifts in tactile perception (Exp.s 4.1.3 and 4.1.5), even though the measurability of such changes appeared somewhat task-sensitive (Exp. 4.1.4).
Finally, a highly complex pattern of correlations between the strength of touch observation-related ERP modulations and interindividual differences in trait empathy associated the automatic sharing of othersâ bodily states with complex emotional and cognitive empathy phenomena. How we respond to othersâ somatic sensations thus appears to be fundamentally linked to how readily we respond emotionally to othersâ mental and emotional states and how easily we can infer those states by intentionally putting ourselves into somebody elseâs shoes. More research will be needed to shed more light on the intricate interplay between low-level resonance mechanisms and higher-order affective and cognitive processes in mediating the empathic understanding of othersâ and the occurrence of appropriate other-related emotional responses
Pathophysiological mechanisms of absence epilepsy: a computational modelling study
A typical absence is a non-convulsive epileptic seizure that is a sole symptom of childhood absence epilepsy (CAE). It is characterised by a generalised hyper-synchronous activity (2.5-5 Hz) of neurons in the thalamocortical network that manifests as a spike and slow-wave discharge (SWD) in the electroencephalogram. Although CAE is not a benign form of epilepsy, its physiological basis is not well understood.
In an attempt to make progress regarding the mechanism of SWDs, I built a large-scale computational model of the thalamocortical network that replicated key cellular and network electric oscillatory behaviours.
Model simulation indicated that there are multiple pathological pathways leading to SWDs. They fell into three categories depending on their network-level effects. Moreover, all SWDs had the same physiological mechanism of generation irrespective of their underlying pathology. They were initiated by an increase in NRT cell bursting prior to the SWD onset. SWDs critically depended on the T-type Ca2+ current (IT) mediated firing in NRT and higher-order thalamocortical relay cells (TCHO), as well as GABAB synaptic receptor-mediated IPSPs in TCHO cells. On the other hand, first-order thalamocortical cells were inhibited during SWDs and did not actively participate in their generation. These cells, however, could promote or disrupt SWD generation if they were hyperpolarised or depolarised, respectively. Importantly, only a minority of active TC cells with a small proportion of them bursting were necessary to ensure the SWD generation. In terms of their relationship to other brain rhythms, simulated SWDs were a product of NRT sleep spindle (6.5-14 Hz) and cortical δ (1-4 Hz) pacemakers and had their oscillation frequency settle between the preferred oscillation frequencies of the two pacemakers with the actual value depending on the cortical bursting intensity. These modelling results are discussed in terms of their implications for understanding CAE and its future research and treatment
Pathophysiological mechanisms of absence epilepsy: a computational modelling study
A typical absence is a non-convulsive epileptic seizure that is a sole symptom of childhood absence epilepsy (CAE). It is characterised by a generalised hyper-synchronous activity (2.5-5 Hz) of neurons in the thalamocortical network that manifests as a spike and slow-wave discharge (SWD) in the electroencephalogram. Although CAE is not a benign form of epilepsy, its physiological basis is not well understood.
In an attempt to make progress regarding the mechanism of SWDs, I built a large-scale computational model of the thalamocortical network that replicated key cellular and network electric oscillatory behaviours.
Model simulation indicated that there are multiple pathological pathways leading to SWDs. They fell into three categories depending on their network-level effects. Moreover, all SWDs had the same physiological mechanism of generation irrespective of their underlying pathology. They were initiated by an increase in NRT cell bursting prior to the SWD onset. SWDs critically depended on the T-type Ca2+ current (IT) mediated firing in NRT and higher-order thalamocortical relay cells (TCHO), as well as GABAB synaptic receptor-mediated IPSPs in TCHO cells. On the other hand, first-order thalamocortical cells were inhibited during SWDs and did not actively participate in their generation. These cells, however, could promote or disrupt SWD generation if they were hyperpolarised or depolarised, respectively. Importantly, only a minority of active TC cells with a small proportion of them bursting were necessary to ensure the SWD generation. In terms of their relationship to other brain rhythms, simulated SWDs were a product of NRT sleep spindle (6.5-14 Hz) and cortical δ (1-4 Hz) pacemakers and had their oscillation frequency settle between the preferred oscillation frequencies of the two pacemakers with the actual value depending on the cortical bursting intensity. These modelling results are discussed in terms of their implications for understanding CAE and its future research and treatment
29th Annual Computational Neuroscience Meeting: CNS*2020
Meeting abstracts
This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests.
Virtual | 18-22 July 202
A model for cerebral cortical neuron group electric activity and its implications for cerebral function
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D
Unified developmental model of maps, complex cells and surround modulation in the primary visual cortex
For human and animal vision, the perception of local visual features can depend on
the spatial arrangement of the surrounding visual stimuli. In the earliest stages of visual
processing this phenomenon is called surround modulation, where the response of
visually selective neurons is influenced by the response of neighboring neurons. Surround
modulation has been implicated in numerous important perceptual phenomena,
such as contour integration and figure-ground segregation. In cats, one of the major
potential neural substrates for surround modulation are lateral connections between
cortical neurons in layer 2/3, which typically contains âcomplexâ cells that appear to
combine responses from âsimpleâ cells in layer 4C. Interestingly, these lateral connections
have also been implicated in the development of functional maps in primary
visual cortex, such as smooth, well-organized maps for the preference of oriented lines.
Together, this evidence suggests a common underlying substrate the lateral interactions
in layer 2/3âas the driving force behind development of orientation maps for
both simple and complex cells, and at the same time expression of surround modulation
in adult animals. However, previously these phenomena have been studied
largely in isolation, and we are not aware of a computational model that can account
for all of them simultaneously and show how they are related. In this thesis we resolve
this problem by building a single, unified computational model that can explain the
development of orientation maps, the development of simple and complex cells, and
surround modulation.
First we build a simple, single-layer model of orientation map development based
on ALISSOM, which has more realistic single cell properties (such as contrast gain
control and contrast invariant orientation tuning) than its predecessor. Then we extend
this model by adding layer 2/3, and show how the model can explain development of
orientation maps of both simple and complex cells. As the last step towards a developmental
model of surround modulation, we replace Mexican-hat-like lateral connectivity
in layer 2/3 of the model with a more realistic configuration based on long-range
excitation and short-range inhibitory cells, extending a simpler model by Judith Law.
The resulting unified model of V1 explains how orientation maps of simple and
complex cells can develop, while individual neurons in the developed model express
realistic orientation tuning and various surround modulation properties. In doing so,
we not only offer a consistent explanation behind all these phenomena, but also create
a very rich model of V1 in which the interactions between various V1 properties can
be studied. The model allows us to formulate several novel predictions that relate the variation of single cell properties to their location in the orientation preference maps
in V1, and we show how these predictions can be tested experimentally. Overall,
this model represents a synthesis of a wide body of experimental evidence, forming a
compact hypothesis for much of the development and behavior of neurons in the visual cortex
Development and encoding of visual statistics in the primary visual cortex
How do circuits in the mammalian cerebral cortex encode properties of the sensory
environment in a way that can drive adaptive behavior? This question is fundamental
to neuroscience, but it has been very difficult to approach directly. Various computational
and theoretical models can explain a wide range of phenomena observed in the
primary visual cortex (V1), including the anatomical organization of its circuits, the
development of functional properties like orientation tuning, and behavioral effects
like surround modulation. However, so far no model has been able to bridge these
levels of description to explain how the machinery that develops directly affects behavior.
Bridging these levels is important, because phenomena at any one specific
level can have many possible explanations, but there are far fewer possibilities to
consider once all of the available evidence is taken into account.
In this thesis we integrate the information gleaned about cortical development, circuit
and cell-type specific interactions, and anatomical, behavioral and electrophysiological
measurements, to develop a computational model of V1 that is constrained
enough to make predictions across multiple levels of description. Through a series
of models incorporating increasing levels of biophysical detail and becoming increasingly
better constrained, we are able to make detailed predictions for the types of
mechanistic interactions required for robust development of cortical maps that have
a realistic anatomical organization, and thereby gain insight into the computations
performed by the primary visual cortex.
The initial models focus on how existing anatomical and electrophysiological knowledge
can be integrated into previously abstract models to give a well-grounded and
highly constrained account of the emergence of pattern-specific tuning in the primary
visual cortex. More detailed models then address the interactions between specific
excitatory and inhibitory cell classes in V1, and what role each cell type may play
during development and function. Finally, we demonstrate how these cell classes
come together to form a circuit that gives rise not only to robust development but
also the development of realistic lateral connectivity patterns. Crucially, these patterns
reflect the statistics of the visual environment to which the model was exposed
during development. This property allows us to explore how the model is able to
capture higher-order information about the environment and use that information to
optimize neural coding and aid the processing of complex visual tasks.
Using this model we can make a number of very specific predictions about the
mechanistic workings of the brain. Specifically, the model predicts a crucial role of
parvalbumin-expressing interneurons in robust development and divisive normalization,
while it implicates somatostatin immunoreactive neurons in mediating longer
range and feature-selective suppression. The model also makes predictions about the
role of these cell classes in efficient neural coding and under what conditions the
model fails to organize. In particular, we show that a tight coupling of activity between
the principal excitatory population and the parvalbumin population is central
to robust and stable responses and organization, which may have implications for
a variety of diseases where parvalbumin interneuron function is impaired, such as
schizophrenia and autism. Further the model explains the switch from facilitatory to
suppressive surround modulation effects as a simple by-product of the facilitating
response function of long-range excitatory connections targeting a specialized class
of inhibitory interneurons. Finally, the model allows us to make predictions about the
statistics that are encoded in the extensive network of long-range intra-areal connectivity
in V1, suggesting that even V1 can capture high-level statistical dependencies
in the visual environment.
The final model represents a comprehensive and well constrained model of the
primary visual cortex, which for the first time can relate the physiological properties
of individual cell classes to their role in development, learning and function. While
the model is specifically tuned for V1, all mechanisms introduced are completely
general, and can be used as a general cortical model, useful for studying phenomena
across the visual cortex and even the cortex as a whole. This work is also highly
relevant for clinical neuroscience, as the cell types studied here have been implicated
in neurological disorders as wide ranging as autism, schizophrenia and Parkinsonâs
disease
- âŚ