Acquisition and Performance of Delayed-response Tasks: a Neural Network Model

Abstract

We study the time evolution of a neural network model as it learns the three stages of a visual delayed-matching-to-sample (DMS) task: identification of the sample, retention during delay, and matching of sample and target, ignoring distractors. We introduce a neurobiologically plausible, uncommitted architecture, comprising an "executive" subnetwork gating connections to and from a "working" layer. The network learns DMS by reinforcement: reward-dependent synaptic plasticity generates task-dependent behaviour. During learning, working layer cells exhibit stimulus specialization and increased tuning of their firing. The emergence of top-down activity is observed, reproducing aspects of prefrontal cortex control on activity in the visual areas of inferior temporal cortex. We observe a lability of neural systems during learning, with a tendency to encode spurious associations. Executive areas are instrumental during learning to prevent such associations; they are also fundamental for the "mature" network to keep passing DMS. In the mature model, the working layer functions as a short-term memory. The mature system is remarkably robust against cell damage and its performance degrades gracefully as damage increases. The model underlines that executive systems, which regulate the flow of information between working memory and sensory areas, are required for passing tests such as DMS. At the behavioural level, the model makes testable predictions about the errors expected from subjects learning the DMS.

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating

“…We believe that gating will be found crucial for hierarchical tasks, just as for complex cognition in general (Gisiger et al, 2005;O'Reilly, 2006). The fact that it has readily evolved in a reinforcement-learning task in a simulated honeybee neural network (Soltoggio et al, 14 2007) supports this idea.…”

A Model for Integrating Elementary Neural Functions into Delayed-Response Behavior

“…The model (see Figure 2) has several components in common with an earlier, simpler network used to reproduce DMS execution [13]: these elements are a working memory (WM), a visual representation area (VR), and an input layer (Input), which simulate parts of the PF, IT, and primary visual cortices, respectively. WM is a two-dimensional layer of excitatory and inhibitory neurons interconnected by short-range projections.…”

“…The “reset WM” unit, when active, suppresses all ongoing activity in layer WM. We have shown earlier that when the G u , G d , and reset WM units obey suitable firing patterns (Figure 3B, gatings G and reset WM), the resulting network can be trained for the DMS task using simple Hebbian [14] and reinforcement-learning algorithms, which it then passes with a high degree of success [13]. …”

Healthy children show gender differences in correlations between nonverbal cognitive ability and brain activation during visual perception

“…The DMS task has three stages: identification of the sample, retention during delay, and the matching of the sample and target in visual perception [9]. All of the stages recruit attentional orienting to objects in the task procedure.…”