A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons
نویسندگان
چکیده
منابع مشابه
Spiking Neural Networks for Cortical Neuronal Spike Train Decoding
Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative t...
متن کاملStrictly Positive-Definite Spike Train Kernels for Point-Process Divergences
Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as...
متن کاملLinking non-binned spike train kernels to several existing spike train metrics
This work presents two kernels which can be applied to sets of spike times. This allows the use of state-of-the-art classification techniques to spike trains. The presented kernels are closely related to several recent and often used spike train metrics. One of the main advantages is that it does not require the spike trains to be binned. A high temporal resolution is thus preserved which is ne...
متن کاملModel-based reinforcement learning with spiking neurons
Behavioural and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. An increasingly explicit set of neuroscientific ideas has been established for habit formation, whereas goal-directed control has only recently started to attract researchers’ attention. While using functional magnetic resonance imaging to ...
متن کاملDelay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs
Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this ...
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: Frontiers in Neuroscience
سال: 2019
ISSN: 1662-453X
DOI: 10.3389/fnins.2019.00252