Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory

Abstract

A commonly observed neural correlate of working memory is firing that persists after the triggering stimulus disappears. Substantial effort has been devoted to understanding the many potential mechanisms that may underlie memory-associated persistent activity. These rely either on the intrinsic properties of individual neurons or on the connectivity within neural circuits to maintain the persistent activity. Nevertheless, it remains unclear which mechanisms are at play in the many brain areas involved in working memory. Herein, we first summarize the palette of different mechanisms that can generate persistent activity. We then discuss recent work that asks which mechanisms underlie persistent activity in different brain areas. Finally, we discuss future studies that might tackle this question further. Our goal is to bridge between the communities of researchers who study either single-neuron biophysical, or neural circuit, mechanisms that can generate the persistent activity that underlies working memory.

Keywords: attractor network; bistability; feedback; neocortex; persistent activity; plateau potential; short-term memory; synaptic transmission.

Figure 1

Memory function created by embedding bistable neurons in feedfoward networks. A network is modeled on the dentate gyrus, which contains a set of bistable excitatory neurons (semilunar granule cells) (Larimer & Strowbridge 2010, Williams et al. 2007) that generate divergent projections onto downstream cells (hilar neurons in the dentate gyrus). Different input patterns can trigger bistable responses in different subsets of bistable cells, leading to different combinations of synaptic input barrages in the downstream neurons. Which input was presented can be determined by monitoring changes in the synaptic tone in a small subgroup of downstream neurons (as in Hyde & Strowbridge 2012 and Larimer & Strowbridge 2010).

Figure 2

Network models capable of generating persistent representations. (a) In discrete attractor models, a population of neurons is described by their firing rates (axes of the diagram). The network dynamics cause movement within this space: At each point, the small arrows indicate the direction in which the population activities move. (This is known as a direction field in mathematics, and one can visualize trajectories of the population by connecting neighboring arrows in a head-to-tail fashion.) Here, all direction arrows point toward either point αor point β, and so the network activity patterns will evolve toward one of these two activity patterns. Which of these patterns gets generated depends on whether the inputs push the network to the left of the marked boundary or the right. This boundary is known as a separatrix. (b) Continuous attractor models are similar to the model in panel a, but now the direction arrows all point toward a continuous line. The network dynamics cause the activity patterns to evolve to points (patterns) on the marked line. (c) In models displaying continuous representations despite time-varying neural activities, the remembered stimulus value is assumed to be encoded in a combination of neural firing rates: in other words, by the projection of the population firing rate vector onto a line (coding line) in the space of neural activities. Here, the dynamical evolution of the neural activities is orthogonal to that coding line, and so the changes in neural firing rates do not change the projection of the firing rate vector onto that line. Consequently, the representation is stably maintained.