When natural scenes are viewed, a multitude of objects that are stable in their environments are brought in and out of view by eye movements. The posterior parietal cortex is crucial for the analysis of space, visual attention and movement 1 . Neurons in one of its subdivisions, the lateral intraparietal area (LIP), have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields)2. We have tested the responses of LIP neurons to stimuli that entered their receptive field by saccades. Neurons had little or no response to stimuli brought into their receptive field by saccades, unless the stimuli were behaviourally significant. We established behavioural significance in two ways: either by making a stable stimulus task-relevant, or by taking advantage of the attentional attraction of an abruptly appearing stimulus. Our results show that under ordinary circumstances the entire visual world is only weakly represented in LIP. The visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.

The representation of visual salience in monkey parietal cortex

http://www.hegde.us/PIIS1364661312002471.pdf

The ventral visual pathway: an expanded neural framework for the processing of object quality

A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Recently described, large-scale neural models have not bridged this gap between neural activity and biological function. In this work, we present a 2.5-million-neuron model of the brain (called “Spaun”) that bridges this gap by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.

http://science.sciencemag.org/content/sci/338/6111/1202.full.pdf

A Large-Scale Model of the Functioning Brain

Dynamic Coding for Cognitive Control in Prefrontal Cortex

Computational strategies used by the brain strongly depend on the amount of information that can be stored in population activity, which in turn strongly depends on the pattern of noise correlations. In vivo, noise correlations tend to be positive and proportional to the similarity in tuning properties. Such correlations are thought to limit information, which has led to the suggestion that decorrelation increases information. In contrast, we found, analytically and numerically, that decorrelation does not imply an increase in information. Instead, the only information-limiting correlations are what we refer to as differential correlations: correlations proportional to the product of the derivatives of the tuning curves. Unfortunately, differential correlations are likely to be very small and buried under correlations that do not limit information, making them particularly difficult to detect. We found, however, that the effect of differential correlations on information can be detected with relatively simple decoders.

Information-limiting correlations

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

/pdf/demixed-principal-component-analysis-of-neural-population-3nqbsx6c84.pdf

Demixed principal component analysis of neural population data

Persistent activity generated in the PFC during the delay period of working memory tasks represents information about stimuli held in memory and determines working memory performance. Alternative models of working memory, depending on the rhythmicity of discharges or exclusively on short-term synaptic plasticity, are inconsistent with the neurophysiological data.Dual Perspectives Companion Paper:Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not, by Mikael Lundqvist, Pawel Herman, and Earl K. Miller.

https://www.jneurosci.org/content/jneuro/38/32/7020.full.pdf

Persistent Spiking Activity Underlies Working Memory.

The prefrontal cortex is known to represent different types of information in working memory. Contrasting theories propose that the dorsal and ventral regions of the lateral prefrontal cortex are innately specialized for the representation of spatial and nonspatial information, respectively (Goldman-Rakic, 1996), or that the two regions are shaped by the demands of cognitive tasks imposed on them (Miller, 2000). To resolve this issue, we recorded from neurons in the two regions, before and at multiple stages of training monkeys on visual working memory tasks. Before training, substantial functional differences were present between the two regions. Dorsal prefrontal cortex exhibited higher overall responsiveness to visual stimuli and higher selectivity for spatial information. After training, stimulus selectivity generally decreased, although dorsal prefrontal cortex retained higher spatial selectivity regardless of task performed. Ventral prefrontal cortex appeared to be affected to a greater extent by the nature of the task. Our results indicate that regional specialization for stimulus selectivity is present in the primate prefrontal cortex regardless of training. Dorsal areas of the prefrontal cortex are inherently organized to represent spatial information, and training has little influence on this spatial bias. Ventral areas are biased toward nonspatial information, although they are more influenced by training both in terms of activation and changes in stimulus selectivity.

https://www.jneurosci.org/content/jneuro/31/17/6266.full.pdf

Stimulus Selectivity in Dorsal and Ventral Prefrontal Cortex after Training in Working Memory Tasks

The ability to learn new tasks requires that new information is integrated into neural systems that already support other behaviors. To study how new information is incorporated into neural representations, we analyzed single-unit recordings from the prefrontal cortex (PFC), a brain region important for task acquisition and working memory, before and after monkeys learned to perform two behavioral tasks. A population-decoding analysis revealed a large increase in task-relevant information, and smaller changes in stimulus-related information, after training. This new information was contained in dynamic patterns of neural activity, with many individual neurons containing the new task-relevant information for only relatively short periods of time in the midst of other large firing rate modulations. Additionally, we found that stimulus information could be decoded with high accuracy only from dorsal PFC, whereas task-relevant information was distributed throughout both dorsal and ventral PFC. These findings help resolve a controversy about whether PFC is innately specialized to process particular types of information or whether its responses are completely determined by task demands by showing there is both regional specialization within PFC that was present before training, as well as more widespread task-relevant information that is a direct result of learning. The results also show that information is incorporated into PFC through the emergence of a small population of highly selective neurons that overlay new signals on top of patterns of activity that contain information about previously encoded variables, which gives insight into how information is coded in neural activity.

/pdf/incorporation-of-new-information-into-prefrontal-cortical-6z1xdlco63.pdf

Incorporation of new information into prefrontal cortical activity after learning working memory tasks

Neurons in a distributed network of cortical and subcortical areas continue to discharge after the presentation and disappearance of stimuli, providing a neural correlate for working memory. While it is thought that the prefrontal cortex plays a central role in this network, the relative contributions of other brain areas are not as well understood. In order to compare the contributions of the dorsolateral prefrontal and posterior parietal cortex, we recorded neurophysiological activity in monkeys trained to perform two different visuo-spatial working memory tasks: a Match/Nonmatch task, and a Spatial Delayed-Match-to-Sample Task. Neurons in both areas exhibited discharges in the delay periods of the tasks that could be classified in two forms. Sustained discharges persisted after the presentation of a stimulus in the receptive field with a constant or declining rate. Anticipatory responses increased in rate during the delay period, often appearing after presentation of a stimulus out of the receptive field. Despite similarities, we uncovered distinct differences between patterns of delay period in each brain area. Only in the prefrontal cortex sustained responses related to the original stimulus survived presentation of a second stimulus, in the context of the Match/Nonmatch task. Our results provide insights on the nature of processing in two areas active during working memory, and on the unique role of the prefrontal cortex in memory maintenance.

/pdf/comparison-of-neural-activity-related-to-working-memory-in-56hsp0abjo.pdf

Xue-Lian Qi

Papers

Demixed principal component analysis of neural population data

Persistent Spiking Activity Underlies Working Memory.

Stimulus Selectivity in Dorsal and Ventral Prefrontal Cortex after Training in Working Memory Tasks

Incorporation of new information into prefrontal cortical activity after learning working memory tasks

Comparison of neural activity related to working memory in primate dorsolateral prefrontal and posterior parietal cortex.