Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm.

We present a neurocomputational controller for robotic manipulation based on the recently developed "neural virtual machine" (NVM). The NVM is a purely neural recurrent architecture that emulates a Turing-complete, purely symbolic virtual machine. We program the NVM with a symbolic algorithm that solves blocks-world restacking problems, and execute it in a robotic simulation environment.

NeuroLISP: High-level symbolic programming with attractor neural networks.

Despite significant improvements in contemporary machine learning, symbolic methods currently outperform artificial neural networks on tasks that involve compositional reasoning, such as goal-directed planning and logical inference. This illustrates a computational explanatory gap between cognitive and neurocomputational algorithms that obscures the neurobiological mechanisms underlying cognition and impedes progress toward human-level artificial intelligence. Because of the strong relationship between cognition and working memory control, we suggest that the cognitive abilities of contemporary neural networks are limited by biologically-implausible working memory systems that rely on persistent activity maintenance and/or temporal nonlocality.

Compositional memory in attractor neural networks with one-step learning.

Compositionality refers to the ability of an intelligent system to construct models out of reusable parts. This is critical for the productivity and generalization of human reasoning, and is considered a necessary ingredient for human-level artificial intelligence. While traditional symbolic methods have proven effective for modeling compositionality, artificial neural networks struggle to learn systematic rules for encoding generalizable structured models.

A programmable neural virtual machine based on a fast store-erase learning rule.

We present a neural architecture that uses a novel local learning rule to represent and execute arbitrary, symbolic programs written in a conventional assembly-like language. This Neural Virtual Machine (NVM) is purely neurocomputational but supports all of the key functionality of a traditional computer architecture. Unlike other programmable neural networks, the NVM uses principles such as fast non-iterative local learning, distributed representation of information, program-independent circuitry, itinerant attractor dynamics, and multiplicative gating for both activity and plasticity.

Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies.

While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks imitation learning relates to this issue. Our discussion is divided into three parts.

Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks.

We introduce mathematical objects that we call "directional fibers," and show how they enable a new strategy for systematically locating fixed points in recurrent neural networks. We analyze this approach mathematically and use computer experiments to show that it consistently locates many fixed points in many networks with arbitrary sizes and unconstrained connection weights. Comparison with a traditional method shows that our strategy is competitive and complementary, often finding larger and distinct sets of fixed points.

A limit-cycle self-organizing map architecture for stable arm control.

Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks.

Towards a multi-level neural architecture that unifies self-intended and imitated arm reaching performance.

Dexterous arm reaching movements are a critical feature that allow human interactions with tools, the environment, and socially with others. Thus the development of a neural architecture providing unified mechanisms for actual, mental, observed and imitated actions could enhance robot performance, enhance human-robot social interactions, and inform specific human brain processes. Here we present a model, including a fronto-parietal network that implements sensorimotor transformations (inverse kinematics, workspace visuo-spatial rotations), for self-intended and imitation performance.