In visual cortex and in a computational model, strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality.
A crucial feature of biological neural architectures is their ability to learn, and unlearn, in response to external stimulation. In this work the authors reproduce this feature in an electronic network composed of strongly correlated electron materials implemented as synaptic devices. This network responds to both excitatory and inhibitory excitations, exhibits associative as well as nonassociative learning, and even displays habituation-like behavior and other aspects of authentic neuronal systems. This opens avenues for both investigating biological behaviors and designing computers with the capacity to learn and remember based on hardware alone.
Data show that, in contrast to the widely held views of limited information transfer in rodent depressing synapses, fast recovering synapses of human neurons can actually transfer substantial amounts of information during spike trains. In addition, human pyramidal neurons are equipped to encode high synaptic information content. Thus, adult human cortical microcircuits relay information at a wider bandwidth than rodent microcircuits..
the mathematical description of evolution in the presence of sexual recombination and weak selection is equivalent to a repeated game between genes played according to the multiplicative weight updates algorithm
Nima Dehghani's insight:
If you are behind PNAS paywall, you can read Quanta Magazine's commentary:
Can a single neuronal spike in 100 billion spikes affect information processing in the human brain? Monteforte and Wolf from Max-Planck Institute for Dynamics and Self-organization show that the answer is ``yes'' and also reveal how that comes about with a novel concept of nonlinear dynamics.
The capabilities of neural networks were extended by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
An open-source library of analytical tools for mapping large-scale patterns of brain activity using cluster computing finds structure in two-photon imaging data from mouse and whole-brain light-sheet functional imaging data from behaving larval zebrafish. Vladimirov et al., also online, describes the light-sheet functional imaging system used here.
Nima Dehghani's insight:
With such surge of data, the lack of theory is the Achilles' heel of neuroscience.
Can computing keep up with the neuroscience data deluge?
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