Spike-timing theory of working memory.

Working memory (WM) is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs) are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds.

Adaptation of prefrontal cortical firing patterns and their fidelity to changes in action-reward contingencies.

Animals adapt action-selection policies when the relationship between possible actions and associated outcomes changes. Prefrontal cortical neurons vary their discharge patterns depending on action choice and rewards received and undoubtedly play a pivotal role in maintaining and adapting action policies. Here, we recorded neurons from the medial precentral subregion of mouse prefrontal cortex to examine neural substrates of goal-directed behavior. Discharge patterns were recorded after animals developed stable action-selection policies, wherein four possible action sequences were invariably related to different reward magnitudes and during adaptation to changes in the action-reward contingencies. During the adaptation period, when the same action sequence resulted in different reward magnitudes, many neurons (38%) exhibited significantly different discharge patterns for identical action sequences, well before reaching the reward site. In addition, trial-to-trial reliability of ensemble pattern production leading up to reward was found to vary both positively and negatively with increases and decreases in reward magnitude, respectively. Pairwise analyses of simultaneously recorded neurons revealed that decreased reliability in part reflected fluctuations between different ensemble activity patterns as opposed to within-pattern variability. Increases in reliability were related to an increased probability of both selecting highly rewarding actions and completing such actions without pause or reversal, whereas decreases in reliability were associated with the opposite pattern. Thus, we suggest that both the spatiotemporal pattern and fidelity of prefrontal cortical discharge are impacted by action-outcome relationships and that each of these features serve to adapt action choices and maintain behaviors leading to reward.

Competitive spiking and indirect entropy minimization of rate code: efficient search for hidden components.

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. 'Spikes' are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.

The mystery of structure and function of sensory processing areas of the neocortex: a resolution.

Many different neural models have been proposed to account for major characteristics of the memory phenomenon family in primates. However, in spite of the large body of neurophysiological, anatomical and behavioral data, there is no direct evidence for supporting one model while falsifying the others. And yet, we can discriminate models based on their complexity and/or their predictive power. In this paper we present a computational framework with our basic assumption that neural information processing is performed by generative networks. A complex architecture is 'derived' by using information-theoretic principles. We find that our approach seems to uncover possible relations among the functional memory units (declarative and implicit memory) and the process of information encoding in primates. The architecture can also be related to the entorhinal-hippocampal loop. An effort is made to form a prototype of this computational architecture and to map it onto the functional units of the neocortex. This mapping leads us to claim that one may gain a better understanding by considering that anatomical and functional layers of the cortex differ. Philosophical consequences regarding the homunculus fallacy are also considered.

Categories, prototypes and memory systems in Alzheimer's disease.

Recent studies indicate that category learning is mediated by multiple neuronal systems. It has been shown that patients with marked impairments in executive functions, explicit memory and procedural learning can categorize the exemplars and prototype of a previously trained category. Simple, self-organizing neuronal networks can explain prototype learning and related dysfunctions in Alzheimer's disease, and provide a model of how prototype learning is mediated by circumscribed mechanisms in the visual cortex.