Learning to represent signals spike by spike

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning to represent signals spike by spike

1Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal 2Group for Neural Theory, INSERM U960, Département d’Etudes Cognitives, Ecole Normale Supérieure, Paris, France 3Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Germany ∗These authors contributed equally †To whom correspondence should be addressed; E-mail: [email protected] or christ...

متن کامل

Distortion of Neural Signals by Spike Coding

Analog neural signals must be converted into spike trains for transmission over electrically leaky axons. This spike encoding and subsequent decoding leads to distortion. We quantify this distortion by deriving approximate expressions for the mean square error between the inputs and outputs of a spiking link. We use integrate-and-fire and Poisson encoders to convert naturalistic stimuli into sp...

متن کامل

Responses of Ensemble Neurons to Spike-Train Signals with Independent Noises: Stochastic Resonance and Spike Variability

Responses have been numerically studied of an ensemble of N (=1, 10, and 100) Hodgkin-Huxley (HH) neurons to coherent spike-train inputs applied with independent Poisson spike-train (ST) noise and Gaussian white noise. Three interrelated issues have been investigated: (1) the difference and the similarity between the effects of the two noises, (2) the size effect of a neuron ensemble on the sig...

متن کامل

Spike timing, calcium signals and synaptic plasticity.

Plasticity at central synapses depends critically on the timing of presynaptic and postsynaptic action potentials. Key initial steps in synaptic plasticity involve the back-propagation of action potentials into the dendritic tree and calcium influx that depends nonlinearly on the action potential and synaptic input. These initial steps are now better understood. In addition, recent studies of p...

متن کامل

Learning optimal spike-based representations

How can neural networks learn to represent information optimally? We answer this question by deriving spiking dynamics and learning dynamics directly from a measure of network performance. We find that a network of integrate-and-fire neurons undergoing Hebbian plasticity can learn an optimal spike-based representation for a linear decoder. The learning rule acts to minimise the membrane potenti...

متن کامل

ذخیره 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: 2020

ISSN: 1553-7358

DOI: 10.1371/journal.pcbi.1007692