1,655 research outputs found
How feedback inhibition shapes spike-timing-dependent plasticity and its implications for recent Schizophrenia models
It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing dependent plasticity and show analytically that the feedback inhibition shortens the time window for LTD during spike-timing dependent plasticity but not for LTP. We then use a realistic GENESIS model to test two hypothesis about interneuron hypofunction and conclude that a reduction in GAD67 is the most likely candidate as the cause for hypofrontality as observed in Schizophrenia
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Synthetic associative learning in engineered multicellular consortia
Associative learning is one of the key mechanisms displayed by living
organisms in order to adapt to their changing environments. It was early
recognized to be a general trait of complex multicellular organisms but also
found in "simpler" ones. It has also been explored within synthetic biology
using molecular circuits that are directly inspired in neural network models of
conditioning. These designs involve complex wiring diagrams to be implemented
within one single cell and the presence of diverse molecular wires become a
challenge that might be very difficult to overcome. Here we present three
alternative circuit designs based on two-cell microbial consortia able to
properly display associative learning responses to two classes of stimuli and
displaying long and short-term memory (i. e. the association can be lost with
time). These designs might be a helpful approach for engineering the human gut
microbiome or even synthetic organoids, defining a new class of decision-making
biological circuits capable of memory and adaptation to changing conditions.
The potential implications and extensions are outlined.Comment: 5 figure
A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity
SummaryWhat causes critical periods (CPs) to open? For the best-studied case, ocular dominance plasticity in primary visual cortex in response to monocular deprivation (MD), the maturation of inhibition is necessary and sufficient. How does inhibition open the CP? We present a theory: the transition from pre-CP to CP plasticity arises because inhibition preferentially suppresses responses to spontaneous relative to visually driven input activity, switching learning cues from internal to external sources. This differs from previous proposals in (1) arguing that the CP can open without changes in plasticity mechanisms when activity patterns become more sensitive to sensory experience through circuit development, and (2) explaining not simply a transition from no plasticity to plasticity, but a change in outcome of MD-induced plasticity from pre-CP to CP. More broadly, hierarchical organization of sensory-motor pathways may develop through a cascade of CPs induced as circuit maturation progresses from “lower” to “higher” cortical areas
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