A hierarchical dynamical map as a basic frame for cortical mapping and its application to priming

An ongoing subthreshold neuronal state established through dynamic coassembling of cortical cells

Ensemble activation of neurons, triggered or spontaneous, sometimes involves a common (overlapping) neuronal population known as core cells. It is speculated that the core cells functioning as a core nucleus have a role in dictating noncore cells' behavior and thus overall local network dynamics. However, the truth and its significance in neuronal information processing still remain to be seen. To address this issue, a neural network model of an early sensory cortical area was simulated. In the network model, noncore cells that have selective responsiveness to sensory features constituted noncore cell assemblies. Core cells, having unselective responsiveness, constituted a single core cell assembly. Sensory stimulation activated neuronal ensembles that were indistinguishable from those activated spontaneously. The core cells were active in every ensemble activation and recruited a changing complement of noncore cells, which varied from spontaneous event to spontaneous event or from triggered event to triggered event. Ensemble activation of neurons was established through what we call dynamic coassembling, in which the core cell assembly and one of the noncore cell assemblies were dynamically linked together. Transient dynamic coassembling frequently and randomly took place during the ongoing (spontaneous) neuronal activity period, and persistent dynamic coassembling did during the stimulus-triggered neuronal activity period. The frequent ongoing activation of core cells mediated through transient dynamic coassembling depolarized noncore cells just below firing threshold, whereby the noncore cells could respond rapidly to sensory stimulation. The persistent dynamic coassembling enhanced the responsiveness of noncore cells. We suggest that the core cells, functioning as a core nucleus, dictate how the noncore cells oscillate at a subthreshold level during the ongoing period and how to respond when stimulated. The transient and persistent dynamic coassembling may be an essential neuronal mechanism for the cortex to prepare and respond effectively to sensory input.

Functional connections between visual areas in extracting object features critical for a visual categorization task

The ability to group visual stimuli into meaningful categories is a fundamental cognitive process. Several experiments have been made to investigate the neural mechanism of visual categorization task. Although experimental evidence is known that prefrontal cortex (PFC) and inferior temporal cortex (ITC) sensitively respond in categorization task, little is known about the functional role of interaction between PFC and ITC in categorization task. To address this issue, we present a model, which performs categorization via an interaction between ITC, PFC, and posterior parietal (PP). Using the model, we show here that the functional connections of synapses between neurons in these areas are organized by the learning depending on a reward that is given only by correct behaviors for the task. We also show that the feedback from PFC to ITC allows the sensitivity enhancement of the ITC neurons encoding the object features critical for the task, and the feedback from PFC to PP works as a spatial attention required for finding object feature critical for the task. The model seems to be comparable with experimental data about categorization.

Cognitive enhancement mediated through postsynaptic actions of norepinephrine on ongoing cortical activity

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief gamma-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.

Neuronal Bases of Perceptual Learning Revealed by a Synaptic Balance Scheme

Our ability to perceive external sensory stimuli improves as we experience the same stimulus repeatedly. This perceptual enhancement, called perceptual learning, has been demonstrated for various sensory systems, such as vision, audition, and somatosensation. I investigated the contribution of lateral excitatory and inhibitory synaptic balance to perceptual learning. I constructed a simple associative neural network model in which sensory features were expressed by the activities of specific cell assemblies. Each neuron is sensitive to a specific sensory feature, and the neurons belonging to the same cell assembly are sensitive to the same feature. During perceptual learning processes, the network was presented repeatedly with a stimulus that was composed of a sensory feature and noise, and the lateral excitatory and inhibitory synaptic connection strengths between neurons were modified according to a pulse-timing-based Hebbian rule. Perceptual learning enhanced the cognitive performance of the network, increasing the signal-to-noise ratio of neuronal activity. I suggest here that the alteration of the synaptic balance may be essential for perceptual learning, especially when the brain tries to adopt the most suitable strategy—signal enhancement, noise reduction, or both—for a given perceptual task.