The first second: models of short-term memory traces in the brain

Cortical computations via transient attractors

The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysiological time scales and those of long-term memory, is typically attributed to persistent neural activity. An alternative mechanism which might allow for such information storage is through temporary modifications to the neural connectivity which decay on the same second-long time scale as the underlying memories. Earlier work that has explored this method has done so by emphasizing one attractor from a limited, pre-defined set. Here, we describe an alternative, a Transient Attractor network, which can learn any pattern presented to it, store several simultaneously, and robustly recall them on demand using targeted probes in a manner reminiscent of Hopfield networks. We hypothesize that such functionality could be usefully embedded within sensory cortex, and allow for a flexibly-gated short-term memory, as well as conferring the ability of the network to perform automatic de-noising, and separation of input signals into distinct perceptual objects. We demonstrate that the stored information can be refreshed to extend storage time, is not sensitive to noise in the system, and can be turned on or off by simple neuromodulation. The diverse capabilities of transient attractors, as well as their resemblance to many features observed in sensory cortex, suggest the possibility that their actions might underlie neural processing in many sensory areas.

Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory

Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

Single Canonical Model of Reflexive Memory and Spatial Attention

Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.

Event time representation in cerebellar mossy fibres arising from the lateral reticular nucleus

Time representation is an important element of cerebellar neural processing, but the mechanisms involved are poorly understood. We demonstrate that the major mossy fibre input system originating from the lateral reticular nucleus (LRN) can represent sensory event timing over hundreds of milliseconds. In vivo, cerebellar-projecting LRN neurons discharge extremely regularly with a clock-like rhythm. In response to stimulation of a wide peripheral receptive field, firing briefly pauses then resumes with precise timing. The precision of post-stimulus spikes and the regularity of firing mean that the stimulus timing is represented by LRN spike timing over hundreds of milliseconds. In an arithmetic progression model of LRN neuron firing, highly predictable post-stimulus spike timing is modulated by changing the variability of the first post-inhibitory spike and of the subsequent interspike intervals. From in vitro analysis we show that the Ca(2+)-activated small-conductance K(+) current (SK) contributes to interspike interval regularity and that the hyperpolarization-activated cation current (I(h)) contributes to short-latency, high-precision post-hyperpolarisation spike timing. Consistent with this, we demonstrate in vivo that resumption of firing becomes more sharply timed after longer stimulus-evoked pauses. Thus, I(h) is a potential conductance that could mediate the precisely timed resumption of firing after the pause. Through the widespread projections of LRN neurons, these properties may enable the LRN to provide precisely timed signals to the cerebellum over a prolonged period following a stimulus, which may also both activate and sustain oscillatory processes in the cerebellar cortex.