3,881 research outputs found
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
Teaching Memory Circuit Elements via Experiment-Based Learning
The class of memory circuit elements which comprises memristive,
memcapacitive, and meminductive systems, is gaining considerable attention in a
broad range of disciplines. This is due to the enormous flexibility these
elements provide in solving diverse problems in analog/neuromorphic and
digital/quantum computation; the possibility to use them in an integrated
computing-memory paradigm, massively-parallel solution of different
optimization problems, learning, neural networks, etc. The time is therefore
ripe to introduce these elements to the next generation of physicists and
engineers with appropriate teaching tools that can be easily implemented in
undergraduate teaching laboratories. In this paper, we suggest the use of
easy-to-build emulators to provide a hands-on experience for the students to
learn the fundamental properties and realize several applications of these
memelements. We provide explicit examples of problems that could be tackled
with these emulators that range in difficulty from the demonstration of the
basic properties of memristive, memcapacitive, and meminductive systems to
logic/computation and cross-bar memory. The emulators can be built from
off-the-shelf components, with a total cost of a few tens of dollars, thus
providing a relatively inexpensive platform for the implementation of these
exercises in the classroom. We anticipate that this experiment-based learning
can be easily adopted and expanded by the instructors with many more case
studies.Comment: IEEE Circuits and Systems Magazine (in press
Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
Here we numerically study a model of excitable media, namely, a network with
occasionally quiet nodes and connection weights that vary with activity on a
short-time scale. Even in the absence of stimuli, this exhibits unstable
dynamics, nonequilibrium phases -including one in which the global activity
wanders irregularly among attractors- and 1/f noise while the system falls into
the most irregular behavior. A net result is resilience which results in an
efficient search in the model attractors space that can explain the origin of
certain phenomenology in neural, genetic and ill-condensed matter systems. By
extensive computer simulation we also address a relation previously conjectured
between observed power-law distributions and the occurrence of a "critical
state" during functionality of (e.g.) cortical networks, and describe the
precise nature of such criticality in the model.Comment: 18 pages, 9 figure
A model of the emergence and evolution of integrated worldviews
It \ud
is proposed that the ability of humans to flourish in diverse \ud
environments and evolve complex cultures reflects the following two \ud
underlying cognitive transitions. The transition from the \ud
coarse-grained associative memory of Homo habilis to the \ud
fine-grained memory of Homo erectus enabled limited \ud
representational redescription of perceptually similar episodes, \ud
abstraction, and analytic thought, the last of which is modeled as \ud
the formation of states and of lattices of properties and contexts \ud
for concepts. The transition to the modern mind of Homo \ud
sapiens is proposed to have resulted from onset of the capacity to \ud
spontaneously and temporarily shift to an associative mode of thought \ud
conducive to interaction amongst seemingly disparate concepts, \ud
modeled as the forging of conjunctions resulting in states of \ud
entanglement. The fruits of associative thought became ingredients \ud
for analytic thought, and vice versa. The ratio of \ud
associative pathways to concepts surpassed a percolation threshold \ud
resulting in the emergence of a self-modifying, integrated internal \ud
model of the world, or worldview
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