Robust information propagation through noisy neural circuits
نویسندگان
چکیده
منابع مشابه
Robust information propagation through noisy neural circuits
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural re...
متن کاملSignal propagation and noisy circuits
The information carried by a signal decays when the signal is corrupted by random noise. This occurs when a message is transmitted over a noisy channel, as well as when a noisy component performs computation. We first study this signal decay in the context of communication and obtain a tight bound on the rate at which information decreases as a signal crosses a noisy channel. We then use this i...
متن کاملImplementing belief propagation in neural circuits
There is growing evidence that neural circuits may employ statistical algorithms for inference and learning. Many such algorithms can be derived from independence diagrams (graphical models) showing causal relationships between random variables. A general algorithm for inference in graphical models is belief propagation, where nodes in a graphical model determine values for random variables by ...
متن کاملInformation Hiding through Noisy Channels
We consider a scenario where information hiding (IH) is performed through noisy channels. There may arise different situations but one of the most common is the case where the legal IH channel is superior to the attacker IH channel. If a special randomized encoding is used by legal users then it is possible to hide information in the noisy components of the cover message. At the same time, the ...
متن کاملRobust design of polyrhythmic neural circuits.
Neural circuit motifs producing coexistent rhythmic patterns are treated as building blocks of multifunctional neuronal networks. We study the robustness of such a motif of inhibitory model neurons to reliably sustain bursting polyrhythms under random perturbations. Without noise, the exponential stability of each of the coexisting rhythms increases with strengthened synaptic coupling, thus ind...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2017
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1005497