Abstract
The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and delays. Current technological barriers make such a mission unachievable. In addition, as a result of the enormous number of required measurements, the estimated network parameters would differ from the original ones. Here we present a versatile experimental technique, which enables the study of recurrent neural networks activity while being capable of dictating the network connectivity and synaptic strengths. This method is based on the observation that the response of neurons depends solely on their recent stimulations, a short-term memory. It allows a long-term scheme of stimulation and recording of a single neuron, to mimic simultaneous activity measurements of neurons in a recurrent network. Utilization of this technique demonstrates the spontaneous emergence of cooperative synchronous oscillations, in particular the coexistence of fast γ and slow δ oscillations, and opens the horizon for the experimental study of other cooperative phenomena within large-scale neural networks.
Highlights
One of the fundamental goals in neuroscience is to understand the mechanisms underlying the emergence of time-dependent collective activities of neural networks (Silva et al, 1991; Gray, 1994; Contreras et al, 1997; Buzsaki and Draguhn, 2004; Buzsaki, 2006; Chialvo, 2010)
The technology of electrophysiological measurements was significantly enhanced during the last decades, there is not yet such a technology which can record from thousands of individual neurons with a single-cell resolution (Marx, 2014) concurrently with realtime gathering of detailed network topology, including synaptic strengths and delays (Pastrana, 2012)
We present and utilize a real-time experimental long-term single-neuron stimulation and recording scheme which allows the study of the collective firing activity of a recurrent neural network, given its synaptic strengths and delays
Summary
One of the fundamental goals in neuroscience is to understand the mechanisms underlying the emergence of time-dependent collective activities of neural networks (Silva et al, 1991; Gray, 1994; Contreras et al, 1997; Buzsaki and Draguhn, 2004; Buzsaki, 2006; Chialvo, 2010). This understanding will shed light on the way the brain reliably analyzes information and generates behavior (Klimesch, 1999; Basar et al, 2001; Wiest and Nicolelis, 2003; Kahana, 2006; Bollimunta et al, 2008; Fries, 2009; Giraud and Poeppel, 2012). The technology of electrophysiological measurements was significantly enhanced during the last decades, there is not yet such a technology which can record from thousands of individual neurons with a single-cell resolution (Marx, 2014) concurrently with realtime gathering of detailed network topology, including synaptic strengths and delays (Pastrana, 2012)
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