Context-sensitive spatio-temporal memory

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism

A Cortical Sparse Distributed Coding Model Linking Mini- and Macrocolumn-Scale Functionality

No generic function for the minicolumn – i.e., one that would apply equally well to all cortical areas and species – has yet been proposed. I propose that the minicolumn does have a generic functionality, which only becomes clear when seen in the context of the function of the higher-level, subsuming unit, the macrocolumn. I propose that: (a) a macrocolumn's function is to store sparse distributed representations of its inputs and to be a recognizer of those inputs; and (b) the generic function of the minicolumn is to enforce macrocolumnar code sparseness. The minicolumn, defined here as a physically localized pool of ∼20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ∼70 active L2/3 cells, assuming ∼70 minicolumns per macrocolumn. I describe an algorithm for activating these codes during both learning and retrievals, which causes more similar inputs to map to more highly intersecting codes, a property which yields ultra-fast (immediate, first-shot) storage and retrieval. The algorithm achieves this by adding an amount of randomness (noise) into the code selection process, which is inversely proportional to an input's familiarity. I propose a possible mapping of the algorithm onto cortical circuitry, and adduce evidence for a neuromodulatory implementation of this familiarity-contingent noise mechanism. The model is distinguished from other recent columnar cortical circuit models in proposing a generic minicolumnar function in which a group of cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of the sparse distributed macrocolumnar code

The Salmonella Mutagenicity Assay: The Stethoscope of Genetic Toxicology for the 21st Century

Objectives: According to the 2007 National Research Council report Toxicology for the Twenty-First Century, modern methods (e.g., "omics," in vitro assays, high-throughput testing, computational methods) will lead to the emergence of a new approach to toxicology. The Salmonella mammalian microsome mutagenicity assay has been central to the field of genetic toxicology since the 1970s. Here we document the paradigm shifts engendered by the assay, the validation and applications of the assay, and how the assay is a model for future in vitro toxicology assays. Data sources: We searched PubMed, Scopus, and Web of Knowledge using key words relevant to the Salmonella assay and additional genotoxicity assays. Data extraction: We merged the citations, removing duplicates, and categorized the papers by year and topic. Data synthesis: The Salmonella assay led to two paradigm shifts: that some carcinogens were mutagens and that some environmental samples (e.g., air, water, soil, food, combustion emissions) were mutagenic. Although there are > 10,000 publications on the Salmonella assay, covering tens of thousands of agents, data on even more agents probably exist in unpublished form, largely as proprietary studies by industry. The Salmonella assay is a model for the development of 21st century in vitro toxicology assays in terms of the establishment of standard procedures, ability to test various agents, transferability across laboratories, validation and testing, and structure-activity analysis. Conclusions: Similar to a stethoscope as a first-line, inexpensive tool in medicine, the Salmonella assay can serve a similar, indispensable role in the foreseeable future of 21st century toxicology

Human-Centered Design Components in Spiral Model to Improve Mobility of Older Adults

As humans grow older, their cognitive needs change more frequently due to distal and proximal life events. Designers and developers need to come up with better designs that integrate older users’ needs in a short period of time with more interaction with the users. Therefore, the positioning of human end users in the center of the design itself is not the key to the success of design artifacts while designing applications for older adults to use a smartphone as a promising tool for journey planner while using public transportation. This study analyzed the use of human-centered design (HCD) components, the spiral model, and the design for failure (DfF) approach to improve the interactions between older users and designers/developers in gathering usability needs in the concept stage and during the development of the app with short iterative cycles. To illustrate the importance of the applied approach, a case study with particular focus on older adults is presented.The results presented in this study are based on “Assistant” project funded by AAL JP, co-funded by the European Union. The authors would like to thank Dr. Stefan Carmien, my colleague in Assistant, for mentoring and for reading and making comments in the earlier versions of this chapter; participating research institutes; funding agencies; and companies from Finland, Spain, Austria, France, and the United Kingdom for their active support throughout the project

It is time to talk about people: a human-centered healthcare system

Examining vulnerabilities within our current healthcare system we propose borrowing two tools from the fields of engineering and design: a) Reason's system approach [1] and b) User-centered design [2,3]. Both approaches are human-centered in that they consider common patterns of human behavior when analyzing systems to identify problems and generate solutions. This paper examines these two human-centered approaches in the context of healthcare. We argue that maintaining a human-centered orientation in clinical care, research, training, and governance is critical to the evolution of an effective and sustainable healthcare system

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

A Neural Model of Episodic and Semantic Spatiotemporal Memory

A neural network model is proposed that forms sparse spatiotemporal memory traces of spatiotemporal events given single occurrences of the events. The traces are distributed in that each individual cell and synapse participates in numerous traces. This sharing of representational substrate provides the basis for similarity-based generalization and thus semantic memory. Simulation results are provided demonstrating that similar spatiotemporal patterns map to similar traces. The model achieves this property by measuring the degree of match, G, between the current input pattern on each time slice and the expected input given the preceding time slices (i.e., temporal context) and then adding an amount of noise, inversely proportional to G, to the process of choosing the internal representation for the current time slice. Thus, if G is small, indicating novelty, we add much noise and the resulting internal representation of the current input pattern has low overlap with any preexisting representations of time slices. If G is large, indicating a familiar event, we add very little noise resulting in reactivation of all or most of the preexisting representation of the input pattern