NeuronFlow Moreira, Orlando; Yousefzadeh, Amirreza; Chersi, Fabian ...
Proceedings of the 23rd Conference on Design, Automation and Test in Europe,
03/2020
Conference Proceeding
Neuronflow is a neuromorphic, many core, data flow architecture that exploits brain-inspired concepts to deliver a scalable event-based processing engine for neuron networks in Live AI applications. ...Its design is inspired by brain biology, but not necessarily biologically plausible. The main design goal is the exploitation of sparsity to dramatically reduce latency and power consumption as required by sensor processing at the Edge.
While increasing evidence points to a critical role for the motor system in language processing, the focus of previous work has been on the linguistic category of verbs. Here we tested whether nouns ...are effective in modulating the motor system and further whether different kinds of nouns - those referring to artifacts or natural items, and items that are graspable or ungraspable - would differentially modulate the system. A Transcranial Magnetic Stimulation (TMS) study was carried out to compare modulation of the motor system when subjects read nouns referring to objects which are Artificial or Natural and which are Graspable or Ungraspable. TMS was applied to the primary motor cortex representation of the first dorsal interosseous (FDI) muscle of the right hand at 150 ms after noun presentation. Analyses of Motor Evoked Potentials (MEPs) revealed that across the duration of the task, nouns referring to graspable artifacts (tools) were associated with significantly greater MEP areas. Analyses of the initial presentation of items revealed a main effect of graspability. The findings are in line with an embodied view of nouns, with MEP measures modulated according to whether nouns referred to natural objects or artifacts (tools), confirming tools as a special class of items in motor terms. Additionally our data support a difference for graspable versus non graspable objects, an effect which for natural objects is restricted to initial presentation of items.
It is now widely accepted that one of the roles of the hippocampus is to maintain episodic spatial representations, while parallel striatal pathways contribute to both declarative and procedural ...value computations by encoding different input-specific outcome predictions. In this paper we investigate the use of these brain mechanisms for action selection, linking them to model-based and model-free controllers for decision making. To this aim we propose a biologically inspired computational model that embodies these theories and explains the functioning of the hippocampal-striatal circuit in a rat navigation task. Its main characteristic is to allow the cooperation of habitual and goal-directed behaviors, with the hippocampus primarily involved in encoding spatial information and simulating possible navigation paths, and the ventral and dorsal striatum involved in learning stimulus-response behaviors and evaluating the reward expectancies associated to predicted locations and sensed stimuli, respectively. The architecture we present employs an unsupervised reinforcement learning rule for the hippocampal-striatal network that is able to build a representation of the environment in which rewarding sites and informative landmarks produce value gradients that are used for planning and decision making. Additionally, it utilizes an arbitration mechanism that balances between exploitation, i.e. stimulus-response behaviors, and mental exploration, i.e. motor imagery processes, based on the intensity and the variability of the responses of striatal neurons. We interpret these results in light of recent experimental data that show anticipatory activations in hippocampal and striatal areas.
We introduce in this paper the principle of Deep Temporal Networks that allow to add time to convolutional networks by allowing deep integration principles not only using spatial information but also ...increasingly large temporal window. The concept can be used for conventional image inputs but also event based data. Although inspired by the architecture of brain that inegrates information over increasingly larger spatial but also temporal scales it can operate on conventional hardware using existing architectures. We introduce preliminary results to show the efficiency of the method. More in-depth results and analysis will be reported soon!
Our everyday, common sense ability to discern the intentions of others' from their motions is fundamental for a successful cooperation in joint action tasks. In this paper we address in a modeling ...study the question of how the ability to understand complex goal-directed action sequences may develop during learning and practice. The model architecture reflects recent neurophysiological findings that suggest the existence of chains of mirror neurons associated with specific goals. These chains may be activated by external events to simulate the consequences of observed actions. Using the mathematical framework of dynamical neural fields to model the dynamics of different neural populations representing goals, action means and contextual cues, we show that such chains may develop based on a local, Hebbian learning rule. We validate the functionality of the learned model in a joint action task in which an observer robot infers the intention of a partner to chose a complementary action sequence.
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