Geometry Of Neural Computation Unifies Working Memory And Planning Part 1

Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. 

Independent modularization refers to a way of managing and organizing things that make managing and executing tasks more efficient and convenient by breaking them down into independent and reusable modules. Memory refers to a person's ability to learn, remember, and use information, which is extremely important for personal development and success.

Although independent modularity and memory may not seem to be directly related there is a close connection between the two. Independent modularization can help us better organize and manage information and break it down into modules that are easy to understand and remember. For example, when we study a course, we can break it down into various modules, such as learning objectives, important concepts, exercises, etc. This will not only help us understand and remember information better but also learn and master knowledge more efficiently.

In addition, independent modularization can also help us better plan and arrange time to make better use of time and resources. When we break tasks into independent modules, we can better estimate the time and resource requirements of each module and prioritize them as needed. This allows us to better control our time, increase productivity, and thus better achieve personal and organizational goals.

To sum up, independent modularity and memory are complementary to each other, and the relationship between the two is very close. Independent modularity can help us better organize and remember information, improving personal and organizational productivity and results, while memory can help us better master and apply information, thereby achieving personal and organizational development and success. Therefore, we should actively cultivate and improve our independent modularization and memory abilities to better achieve personal and organizational goals. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thus improving brain vitality and endurance.

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We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. 

Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.

Working memory | computational model | neural network | representational geometry.

In time-varying environments, flexible cognition requires the ability to store and combine information across time to appropriately guide behavior. In commonly used delay task paradigms, a transient sensory stimulus provides information, which the agent must maintain internally across a seconds-long mnemonic delay to guide a future response (1–4). 

Working memory is a core cognitive function for the active maintenance and manipulation of task-relevant information for subsequent use. To guide flexible behavior, the contents of working memory must interface with other cognitive functions, such as planning and context-dependent decision-making. Yet within neuroscience, these functions have been studied largely independently, and it remains poorly understood how the brain coordinates these processes in the service of goal-directed behavior.

Internal representations related to cognitive states can be revealed through recording neural activity during delayed tasks in animals and humans. Neurons in the prefrontal cortex exhibit content-selective activity patterns during mnemonic delays of working memory tasks (5, 6). Selective delay activity during working memory is most commonly interpreted and implemented in computational models as representing features of sensory stimuli, which can be processed to guide a later response. 

In the dominant conceptual framework, the proposed cognitive strategy thereby uses working memory representations that are fundamentally sensory (5, 6). The sensory strategy for working memory has been challenged by observations of prefrontal delay activity that better correlate with diverse task variables, including actions, expected stimuli, and rules (2, 3, 7). 

Furthermore, a growing literature characterizes substantial nonlinear mixed selectivity of features in the prefrontal cortex, which can in principle support context-dependent behavior (8–10). These diverse observations suggest that a more unified framework is needed to account for the computational roles of working memory, planning, and context-dependent decision-making.

Computational modeling has been fruitfully applied to examine potential neural circuit mechanisms supporting cognitive functions, including working memory and decision-making (11). Working memory functions are commonly modeled with distinct modular circuits, which do not account for mixed selectivity in the prefrontal cortex and assume that working memory maintains sensory representations (11, 12). 

A complementary modeling approach utilizes artificial neural network models, which are trained to perform cognitive tasks (13). In contrast to hand-designed models, task-trained recurrent neural network (RNN) models can perform delay tasks without requiring assumptions about structured circuit architectures or the form of working memory representations (14). 

This approach is, therefore, well suited to examine computational mechanisms through which working memory representations can support flexible computations (4, 15–18).

Significance

Linking observed states of neural circuits to beliefs, actions, and plans is a core goal of systems neuroscience, but doing so presents substantial challenges for experimental design, analysis, and theory. 

Particularly in the domain of delay tasks, the representational logic of neural ensembles has been widely debated. Using a delay task that allows independent control of computational demands on a trial-wise basis, we identify a putative delay representation that reflects plans regarding upcoming expected stimuli. Contingency representations enable efficient mappings to responses for a variety of disparate delay tasks. 

In task-optimized neural networks, we explore how contingency representations relate to observed phenomena from the neurophysiological working memory literature. Lastly, we demonstrate that human behavior is consistent with contingency representations.

In this study, we investigate the implications of a cognitive strategy in which internal states represent plans rather than perceptions or actions. Specifically, our theoretical framework defines contingency states based on how future behaviors depend on upcoming events. We designed a task paradigm, conditional delayed logic (CDL), to dissociate contingency-based strategies from sensory or action-based strategies for working memory. 

RNN models trained on the CDL task develop enrichment of contingency in their state representations, which is reflected in the geometric structure of population activity patterns. The model’s internal representations capture diverse phenomena of neural activity observed during working memory. We tested human behavior on the CDL task and found key signatures of a contingency-based strategy in response-time behavior. 

Lastly, we provide falsifiable predictions for neural activity to distinguish contingency representations from alternative computational schemes. Taken together, our study presents a theoretical framework for how working memory supports planning for temporally extended cognition and flexible behavior, which explains disparate behavioral and neural observations and is experimentally testable.

Results

CDL Task. In many commonly used delay tasks, there are correlations among sensory stimuli, responses, computational demands, or rules that prevent dissociable attribution of neural activity to specific cognitive task variables (1, 19). We, therefore, sought a task that involves 1) computation on informational inputs separated by a delay; 2) exact intermediate computational states that reoccur frequently; 3) trials for which the correct response can be predicted during the delay on some trials and not on others; and 4) decorrelation of action, sensory, rule, and computational-state information.

To meet these demands, we designed the CDL task, which applies a binary classification to two binary stimuli separated by a delay (Fig. 1A). We refer to the delay and post-delay stimuli as “cue A” and “cue B,” respectively. The rules can be defined as Boolean logical operations, and one rule is presented tonically throughout each trial’s duration. 

On each trial, the agent is presented with a task rule, cue A, and cue B and must generate an associated response. For example, in the OR rule, the agent’s response should be “one” if either cue is equal to one. From 16 possible two-bit binary classifications, we will focus on 10 rules (Fig. 1B) for which the response is dependent on cues but independent of cue order. 

The final 10 rules include the logical operators (N)OR, (N)AND, and X(N)OR as well as (Anti-)Memory and (Anti-)Report. In Memory, the agent must respond with the cue A stimulus for that trial, and in Report, the agent must respond with the cue B stimulus for that trial. In both cases, “Anti-” signals an inversion of the output. Rule, cue A, and cue B are varied randomly across trials.

Contingency Representations. To perform the CDL task, the agent must maintain a representation of task information across the delay. One might expect the agent to maintain the stimulus identity of cue A, which would be sufficient to solve all trials. In this “sensory strategy,” which is commonly assumed in models of working memory, the task-relevant information is maintained across the delay to guide a response in conjunction with information in cue B and rule (5, 20). 

An alternative “action strategy” is possible for trial conditions in which the response can be preplanned during the delay (2, 21). Furthermore, the agent may represent the rule identity, which is presented during the delay (3). These representations are not exclusive.

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