Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Conceptualising researchers’ risks and synthesising strategies for engaging with those risks: articulating an agenda for apprehending scholars’ precarious positions

This chapter introduces this edited volume by articulating an agenda for apprehending scholars’ multiple and multifaceted precarious positions that constitute a springboard for the subsequent chapters’ assorted engagements with the proposition of researchers at risk. This agenda is framed in terms of a dual focus on presenting selected conceptualisations of researchers’ risks, and of synthesising particular strategies that researchers have demonstrated to be effective in engaging with those risks. These conceptualisations and strategies constitute in turn empirically grounded manifestations of the precarity, jeopardy and uncertainty that accompany certain aspects of contemporary researchers’ work. The chapter also outlines the clustering of the subsequent chapters into four parts, each directed at a different form of research risk: researchers’ identities; researchers’ professions; subject matter; and conflict-laden locations

Mixed reality with a collaborative information system

In this paper, we present a mixed reality environment (MIXER) for immersive interactions. MIXER is an agent based collaborative information system displaying hybrid reality merging interactive computer graphics and real objects. MIXER is an agent based collaborative information system displaying hybrid reality merging interactive computer graphics and real objects. The system comprises a sensor subsystem, a network subsystem and an interaction subsystem. Related issues to the concept of mixed interaction, including human aware computing, mixed reality fusion, agent based systems, collaborative scalable learning in distributed systems, QoE-QoS balanced management and information security, are discussed. We propose a system architecture to perform networked mixed reality fusion for Ambient Interaction. The components of the mixed reality suit to perform human aware interaction are Interaction Space, Motion Monitoring, Action and Scenario Synthesisers, Script Generator, Knowledge Assistant Systems, Scenario Display, and a Mixed Reality Module. Thus, MIXER as an integrated system can provide a comprehensive human-centered mixed reality suite for advanced Virtual Reality and Augmented Reality applications such as therapy, training, and driving simulations.15 page(s