Behavioral plasticity through the modulation of switch neurons

DART: Diversity-enhanced Autonomy in Robot Teams

This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. Although current approaches to multi-robot coordination have been successful in structured, well-understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. Although robot hardware has advanced significantly in the past decade, the way we solve multi-robot problems has not. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which uses a diverse set of control policies that work together synergistically within the same team of robots. Such an approach will make multi-robot systems more robust in unstructured and uncertain environments, such as in disaster response, environmental monitoring, and military applications, and allow multi-robot systems to extend beyond the highly structured and highly controlled environments where they are successful today.

A loop-based neural architecture for structured behavior encoding and decoding

We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research.

Differential Contributions of Ventral and Dorsal Striatum to Early and Late Phases of Cognitive Set Reconfiguration

Flexible decision-making, a defining feature of human cognition, is typically thought of as a canonical pFC function. Recent work suggests that the striatum may participate as well; however, its role in this process is not well understood. We recorded activity of neurons in both the ventral (VS) and dorsal (DS) striatum while rhesus macaques performed a version of the Wisconsin Card Sorting Test, a classic test of flexibility. Our version of the task involved a trial-and-error phase before monkeys could identify the correct rule on each block. We observed changes in firing rate in both regions when monkeys switched rules. Specifically, VS neurons demonstrated switch-related activity early in the trial-and-error period when the rule needed to be updated, and a portion of these neurons signaled information about the switch context (i.e., whether the switch was intradimensional or extradimensional). Neurons in both VS and DS demonstrated switch-related activity at the end of the trial-and-error period, immediately before the rule was fully established and maintained, but these signals did not carry any information about switch context. We also observed associative learning signals (i.e., specific responses to options associated with rewards in the presentation period before choice) that followed the same pattern as switch signals (early in VS, later in DS). Taken together, these results endorse the idea that the striatum participates directly in cognitive set reconfiguration and suggest that single neurons in the striatum may contribute to a functional handoff from the VS to the DS during reconfiguration processes.