Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory

Abstract

We propose a neural network model for a category-association task. By simulating the model, neuronal relevance of cortical interactions to recalling long-term memory was investigated. The model consists of the left and right hemispheres, each of which has IT (inferotemporal cortex) and PC (prefrontal cortex) networks. Information about visual features and their categories were encoded into point attractors of the IT and PC networks, respectively. In the task, the IT network of the right hemisphere was stimulated with a cue feature. After a delay period, the IT network of the left hemisphere was simultaneously stimulated with the choice feature and an irrelevant feature. The cue and choice features belong to the same category, while the irrelevant feature belongs to another category. To complete the task, the IT network must select the point attractor corresponding to the choice feature. We demonstrate that the top-down pathway (PC-to-IT) triggers the retrieval of long-term memory of the choice feature from the IT, and the bottom-up pathway (IT-to-PC) contributes to the maintenance of the retrieved memory during the delay period. The key mechanism for the retrieval and maintenance of that memory is the dynamic linkage of attractors across separate cortical networks. We show that a single hemisphere is sufficient for the memory retrieval, but it is advantageous to use the two hemispheres because the retrieved memory is thereby retained with greater reliability until the brain chooses the choice feature.

Neuronal Bases of Perceptual Learning Revealed by a Synaptic Balance Scheme

“…This neuronal suppression allows the network state to escape from the basin of a point attractor in which the current network state is being trapped, and thus enables the network to easily respond to an applied stimulus. (For details, see Hoshino et al, 1996Hoshino et al, , 1997Hoshino et al, , 1998Hoshino et al, , 2001Hoshino et al, , 2002.) Such a self-inhibition by neighboring inhibitory interneurons has in fact been found in the visual cortex (Martin, 2002) To reduce total simulation time, I used "one-shot" learning where the ve sensory features were presented only once in their memorization process (see Figure 2b).…”

“…After the stimulus is switched off, the network returns to the ongoing spontaneous state. The dynamic property of the ongoing spontaneous state and its relevance to neuronal information processing have been investigated in detail (Hoshino, Kashimori, & Kambara, 1996Hoshino, Usuba, Kashimori, & Kambara, 1997;Hoshino et al, 2001;Hoshino et al, 2002).…”

“…This idea comes from the notion that there is a close relationship between an increase in the signal-to-noise ratio of neuronal activity and an improvement in cognitive performance (Gilbert, 1994). Other performance measures, such as reaction times to or the detectability of stimuli, which are often used for assessing the performance of associative neural networks (Hoshino et al, 2001;Hoshino, Zheng, & Kuroiwa, 2002), might also be possible. However, as will be shown in later sections, the signal-to-noise ratio of neuronal activity can clearly demonstrate the relevance of the synaptic balance to perceptual learning.…”

Cognitive Enhancement Mediated Through Postsynaptic Actions of Norepinephrine on Ongoing Cortical Activity

“…Pulvermüller and colleagues (Pulvermüller et al, 1999) have suggested that cortical oscillations at higher frequencies reflect the presence of learned associative representations. The dynamic cell assemblies could express such learned associative representations as well, because these assemblies can be created through self-organized (Hebbian) learning processes (Hoshino, Kashimori, & Kambara, 1998;Hoshino, Inoue, Kashimori, & Kambara, 2001;Hoshino, Zheng, & Kuroiwa, 2002). We suggest that such learned representations may be frequently accessed under the ongoing state as temporal formation of each dynamic cell assembly, which is coordinated by synchronous γ -busts among pyramidal cells.…”

An Ongoing Subthreshold Neuronal State Established Through Dynamic Coassembling of Cortical Cells

“…The network has ongoing (spontaneous) neuronal activity, where individual dynamic core and noncore cell assemblies could emerge as attractors in the network dynamics in a temporal and random manner (Hoshino, Kashimori, & Kambara, 1996;Hoshino, Usuba, Kashimori, & Kambara, 1997;Hoshino, Inoue, Kashimori, & Kambara, 2001;Hoshino, Zheng, & Kuroiwa, 2003;Hoshino, 2004Hoshino, , 2005Hoshino, , 2006. Transient dynamic linking between the core and one of the noncore cell assemblies, called dynamic coassembling here, frequently and randomly takes place during the ongoing period.…”