A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Cognitive Control

Humans and other primates have a remarkable ability to perform a wide range of tasks and behaviors, even novel ones, in order to achieve their goals. Further, they are able to shift flexibly among these behaviors as the contexts demand. Cognitive control is the function at the base of this remarkable behavioral generativity and flexibility. The present review provides a survey of current research on cognitive control focusing on two of its primary features within a control systems framework: (a) the ability to select new behaviors based on context and (b) the ability to monitor ongoing behavior and adjust accordingly. Throughout, the review places an emphasis on how differences in the content and structure of task representations affect these core features of cognitive control.

Predicted utility modulates working memory fidelity in the brain

The predicted utility of information stored in working memory (WM) is hypothesized to influence the strategic allocation of WM resources. Prior work has shown that when information is prioritized, it is remembered with greater precision relative to other remembered items. However, these paradigms often complicate interpretation of the effects of predicted utility on item fidelity due to a concurrent memory load. Likewise, no fMRI studies have examined whether the predicted utility of an item modulates fidelity in the neural representation of items during the memory delay without a concurrent load. In the current study, we used fMRI to investigate whether predicted utility influences fidelity of WM representations in the brain. Using a generative model multivoxel analysis approach to estimate the quality of remembered representations across predicted utility conditions, we observed that items with greater predicted utility are maintained in memory with greater fidelity, even when they are the only item being maintained. Further, we found that this pattern follows a parametric relationship where more predicted utility corresponded to greater fidelity. These precision differences could not be accounted for based on a redistribution of resources among already-remembered items. Rather, we interpret these results in terms of a gating mechanism that allows for pre-allocation of resources based on predicted value alone. This evidence supports a theoretical distinction between resource allocation that occurs as a result of load and resource pre-allocation that occurs as a result of predicted utility.

Content or status: Frontal and posterior cortical representations of object category and upcoming task goals in working memory

To optimize task sequences, the brain must differentiate between current and prospective goals. We previously showed that currently and prospectively relevant object representations in working memory can be dissociated within object-selective cortex. Based on other recent studies indicating that a range of brain areas may be involved in distinguishing between currently relevant and prospectively relevant information in working memory, here we conducted multivoxel pattern analyses of fMRI activity in additional posterior areas (specifically early visual cortex and the intraparietal sulcus) as well as frontal areas (specifically the frontal eye fields and lateral prefrontal cortex). We assessed whether these areas represent the memory content, the current versus prospective status of the memory, or both. On each trial, participants memorized an object drawn from three different categories. The object was the target for either a first task (currently relevant), a second task (prospectively relevant), or for neither task (irrelevant). The results revealed a division of labor across brain regions: While posterior areas preferentially coded for content (i.e., the category), frontal areas carried information about the current versus prospective relevance status of the memory, irrespective of the category. Intraparietal sulcus revealed both strong category- and status-sensitivity, consistent with its hub function of combining stimulus and priority signals. Furthermore, cross-decoding analyses revealed that while current and prospective representations were similar prior to search, they became dissimilar during search, in posterior as well as frontal areas. The findings provide further evidence for a dissociation between content and control networks in working memory.

Superresolution imaging reveals activity-dependent plasticity of axon morphology linked to changes in action potential conduction velocity

Axons convey information to nearby and distant cells, and the time it takes for action potentials (APs) to reach their targets governs the timing of information transfer in neural circuits. In the unmyelinated axons of hippocampus, the conduction speed of APs depends crucially on axon diameters, which vary widely. However, it is not known whether axon diameters are dynamic and regulated by activity-dependent mechanisms. Using time-lapse superresolution microscopy in brain slices, we report that axons grow wider after high-frequency AP firing: synaptic boutons undergo a rapid enlargement, which is mostly transient, whereas axon shafts show a more delayed and progressive increase in diameter. Simulations of AP propagation incorporating these morphological dynamics predicted bidirectional effects on AP conduction speed. The predictions were confirmed by electrophysiological experiments, revealing a phase of slowed down AP conduction, which is linked to the transient enlargement of the synaptic boutons, followed by a sustained increase in conduction speed that accompanies the axon shaft widening induced by high-frequency AP firing. Taken together, our study outlines a morphological plasticity mechanism for dynamically fine-tuning AP conduction velocity, which potentially has wide implications for the temporal transfer of information in the brain.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.