13,635 research outputs found
Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease
This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer
A feedback model of perceptual learning and categorisation
Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise
Time-delayed feedback in neurosystems
The influence of time delay in systems of two coupled excitable neurons is
studied in the framework of the FitzHugh-Nagumo model. Time-delay can occur in
the coupling between neurons or in a self-feedback loop. The stochastic
synchronization of instantaneously coupled neurons under the influence of white
noise can be deliberately controlled by local time-delayed feedback. By
appropriate choice of the delay time synchronization can be either enhanced or
suppressed. In delay-coupled neurons, antiphase oscillations can be induced for
sufficiently large delay and coupling strength. The additional application of
time-delayed self-feedback leads to complex scenarios of synchronized in-phase
or antiphase oscillations, bursting patterns, or amplitude death.Comment: 13 pages, 13 figure
Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.
Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays
Interior maps in posterior pareital cortex
The posterior parietal cortex (PPC), historically believed to be a sensory structure, is now viewed as an area important for sensory-motor integration. Among its functions is the forming of intentions, that is, high-level cognitive plans for movement. There is a map of intentions within the PPC, with different subregions dedicated to the planning of eye movements, reaching movements, and grasping movements. These areas appear to be specialized for the multisensory integration and coordinate transformations required to convert sensory input to motor output. In several subregions of the PPC, these operations are facilitated by the use of a common distributed space representation that is independent of both sensory input and motor output. Attention and learning effects are also evident in the PPC. However, these effects may be general to cortex and operate in the PPC in the context of sensory-motor transformations
Spontaneous Local Gamma Oscillation Selectively Enhances Neural Network Responsiveness
Synchronized oscillation is very commonly observed in many neuronal systems and
might play an important role in the response properties of the system. We have
studied how the spontaneous oscillatory activity affects the responsiveness of a
neuronal network, using a neural network model of the visual cortex built from
Hodgkin-Huxley type excitatory (E-) and inhibitory (I-) neurons. When the
isotropic local E-I and I-E synaptic connections were sufficiently strong, the
network commonly generated gamma frequency oscillatory firing patterns in
response to random feed-forward (FF) input spikes. This spontaneous oscillatory
network activity injects a periodic local current that could amplify a weak
synaptic input and enhance the network's responsiveness. When E-E
connections were added, we found that the strength of oscillation can be
modulated by varying the FF input strength without any changes in single neuron
properties or interneuron connectivity. The response modulation is proportional
to the oscillation strength, which leads to self-regulation such that the
cortical network selectively amplifies various FF inputs according to its
strength, without requiring any adaptation mechanism. We show that this
selective cortical amplification is controlled by E-E cell interactions. We also
found that this response amplification is spatially localized, which suggests
that the responsiveness modulation may also be spatially selective. This
suggests a generalized mechanism by which neural oscillatory activity can
enhance the selectivity of a neural network to FF inputs
- …