Stimulus statistics shape oscillations in nonlinear recurrent neural networks.
نویسندگان
چکیده
Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.
منابع مشابه
Dynamics of driven recurrent networks of ON and OFF cells.
A globally coupled network of ON and OFF cells is studied using neural field theory. ON cells increase their activity when the amplitude of an external stimulus increases, while OFF cells do the opposite given the same stimulus. Theory predicts that, without input, multiple transitions to oscillations can occur depending on feedback delay and the difference between ON and OFF resting states. St...
متن کاملMulti-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...
متن کاملIX TELEKOMUNIKACIONI FORUM TELFOR'2001, Beograd, 20-22.11.2001.god. NEURAL NETWORKS BASED MODEL OF A HIGHLY NONLINEAR PROCESS
In this paper two problems will be analyzed: First, the obtain the more accurate prediction than using traditional linear or nonlinear regression approach. For this purpose we shall use the tree layers NN based on Levenberg-Marquardt algorithm. Second, it is known that recurrent neural networks (RNN) are very useful in nonlinear process modeling. Consequently, the trend, i.e. a line of general ...
متن کاملAn efficient one-layer recurrent neural network for solving a class of nonsmooth optimization problems
Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...
متن کاملComputational design and nonlinear dynamics of recurrent network models of the primary visual cortex
The recurrent neural interaction in the primary visual cortex makes its outputs complex nonlinear functions of its inputs. This nonlinear transform serves the role of pre-attentive visual segmentation, i.e., the autonomous transformation from visual inputs to processed outputs that selectively emphasize certain features (e.g., pop-out features) for segmentation. Understanding the nonlinear dyna...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- The Journal of neuroscience : the official journal of the Society for Neuroscience
دوره 35 7 شماره
صفحات -
تاریخ انتشار 2015