Posterior parietal cortex (PPC) activity correlates with monkeys' decisions during visual discrimination and categorization tasks. However, recent work has questioned whether decision-correlated PPC ...activity plays a causal role in such decisions. That study focused on PPC's contribution to motor aspects of decisions (deciding where to move), but not sensory evaluation aspects (deciding what you are looking at). We employed reversible inactivation to compare PPC's contributions to motor and sensory aspects of decisions. Inactivation affected both aspects of behavior, but preferentially impaired decisions when visual stimuli, rather than motor response targets, were in the inactivated visual field. This demonstrates a causal role for PPC in decision-making, with preferential involvement in evaluating attended task-relevant sensory stimuli compared with motor planning.
Throughout the history of modern neuroscience, the parietal cortex has been associated with a wide array of sensory, motor, and cognitive functions. The use of non-human primates as a model organism ...has been instrumental in our current understanding of how areas in the posterior parietal cortex (PPC) modulate our perception and influence our behavior. In this Perspective, we highlight a series of influential studies over the last five decades examining the role of the PPC in visual perception and motor planning. We also integrate long-standing views of PPC functions with more recent evidence to propose a more general model framework to explain integrative sensory, motor, and cognitive functions of the PPC.
Freedman and Ibos review how past debates about the posterior parietal cortex (PPC) led to the development of recent theories. Based on this review and recent advances in the field, they propose a novel integrative comparative framework.
Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. ...This phenomenon is the result of “catastrophic forgetting,” in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance. Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting. Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly nonoverlapping patterns of units are active for any one task. This method is easy to implement, requires little computational overhead, and allows ANNs to maintain high performance across large numbers of sequentially presented tasks, particularly when combined with weight stabilization. We show that this method works for both feedforward and recurrent network architectures, trained using either supervised or reinforcement-based learning. This suggests that using multiple, complementary methods, akin to what is believed to occur in the brain, can be a highly effective strategy to support continual learning.
Categorization is our ability to flexibly assign sensory stimuli into discrete, behaviorally relevant groupings. Categorical decisions can be used to study decision making more generally by ...dissociating category identity of stimuli from the actions subjects use to signal their decisions. Here we discuss the evidence for such abstract categorical encoding in the primate brain and consider the relationship with other perceptual decision paradigms. Recent work on visual categorization has examined neuronal activity across a hierarchically organized network of cortical areas in monkeys trained to group visual stimuli into arbitrary categories. This has revealed a transformation of visual-feature encoding in early visual cortical areas into more flexible categorical representations in downstream parietal and prefrontal areas. These neuronal category representations are encoded as abstract internal cognitive states because they are not rigidly linked with either specific sensory stimuli or the actions that the monkeys use to signal their categorical choices.
Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of ...intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The primate visual system consists of multiple hierarchically organized cortical areas, each specialized for processing distinct aspects of the visual scene. For example, color and form are encoded ...in ventral pathway areas such as V4 and inferior temporal cortex, while motion is preferentially processed in dorsal pathway areas such as the middle temporal area. Such representations often need to be integrated perceptually to solve tasks that depend on multiple features. We tested the hypothesis that the lateral intraparietal area (LIP) integrates disparate task-relevant visual features by recording from LIP neurons in monkeys trained to identify target stimuli composed of conjunctions of color and motion features. We show that LIP neurons exhibit integrative representations of both color and motion features when they are task relevant and task-dependent shifts of both direction and color tuning. This suggests that LIP plays a role in flexibly integrating task-relevant sensory signals.
•LIP neurons encode both color and direction when those features are task relevant•LIP shows task-dependent shifts in visual color and motion direction tuning•Encoding in LIP is consistent with integration of inputs from upstream visual areas
Ibos and Freedman show that posterior parietal cortex neurons encode both color and motion direction when those features are behaviorally relevant. Neurons also showed task-dependent shifts in their color and direction tuning, consistent with flexible integration of upstream visual inputs.
Recently it has been proposed that information in working memory (WM) may not always be stored in persistent neuronal activity but can be maintained in 'activity-silent' hidden states, such as ...synaptic efficacies endowed with short-term synaptic plasticity. To test this idea computationally, we investigated recurrent neural network models trained to perform several WM-dependent tasks, in which WM representation emerges from learning and is not a priori assumed to depend on self-sustained persistent activity. We found that short-term synaptic plasticity can support the short-term maintenance of information, provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent activity naturally emerges from learning, and the amount of persistent activity scales with the degree of manipulation required. These results shed insight into the current debate on WM encoding and suggest that persistent activity can vary markedly between short-term memory tasks with different cognitive demands.
Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural ...recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a “neural landscape” consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.
•Recurrent networks trained to perform DMC tasks exhibit robust transience dynamics•Dynamics consist of stable and slow states connected by robust trajectory tunnels•Models’ neural activities are remarkably similar to recordings from LIP and PFC•Trained RNNs replicate categorization studies with multiple categories
Chaisangmongkon et al. present a recurrent neural network model of primate fronto-parietal network that can capture various phenomena from neurophysiological experiments in delayed match-to-category tasks.
Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes ...neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach-generalized multilinear models-to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.
Single-molecule techniques are being developed with the exciting prospect of revolutionizing the healthcare industry by generating vast amounts of genetic and proteomic data. One exceptionally ...promising route is in the use of nanopore sensors. However, a well-known complexity is that detection and capture is predominantly diffusion limited. This problem is compounded when taking into account the capture volume of a nanopore, typically 10(8)-10(10) times smaller than the sample volume. To rectify this disproportionate ratio, we demonstrate a simple, yet powerful, method based on coupling single-molecule dielectrophoretic trapping to nanopore sensing. We show that DNA can be captured from a controllable, but typically much larger, volume and concentrated at the tip of a metallic nanopore. This enables the detection of single molecules at concentrations as low as 5 fM, which is approximately a 10(3) reduction in the limit of detection compared with existing methods, while still maintaining efficient throughput.
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