The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Abstract

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.

Experimental model of neuronal network learning in dissociated hippocampal cultures

“…Диссоциированные культуры нейрональных клеток, выращенные на мультиэлектродных матрицах, представляют собой одну из современных моделей исследования механизмов кодирования информации, нейрональной пластичности, обучения и памяти в мозге на сетевом уровне in vitro [1, 2,3]. Планарные микроэлектроды таких мультиэлектродных матриц позволяют непрерывно регистрировать биоэлектрическую активность клеток и одновременно воздействовать электрическими стимулами на клетки в разных участках нейронной сети.…”

“…Однако, при частоте стимуляции 1-5 Гц большинство стимулов не вызывает пачек импульсов или ответ сети со временем уменьшается [13,14,3]. Показано, что низкочастотная стимуляция не вызывает ни кратковременных ни долгосрочных изменений функциональной структуры нейронной сети и ответа на стимул [15].…”

Study of Stimulus-Induced Plasticity in Neural Networks Cultured in Microfluidic Chips

“…Dissociated neural cell cultures are widely used as an experimental model for long-term studies of synaptic plasticity in neuronal networks, learning and memory [1][2][3][4][5][6][7][8][9][10]. This model makes it possible to carry out longterm monitoring of bioelectrical functional activity and morphological changes in neural networks.…”

“…There are a significant number of studies devoted to activity-induced plasticity in dissociated brain cell networks cultured for a long time on microelectrode arrays. The most common electrical stimulation protocols are closedloop stimulation (stimulation depending on network responses) [1][2][3][4], low-frequency stimulation (1-0.5 Hz) [5,6] and high frequency tetanic stimulation [7][8][9][10] based on spike timing dependent plasticity (STDP).…”

Bioartificial Brains and Mobile Robots

“…The proposed paradigm represents an innovative, simplified and controllable bidirectional closed loop system where it is possible to investigate the dynamic and adaptive properties of a neural population interacting with an external environment by means of an artificial body (i.e., the mobile robot). Similar closed-loop approaches have been used to quantify the complexity of neural dynamics (Kositsky et al, 2009), to investigate the transition among different electrophysiological regimes (Wagenaar et al, 2005) and to investigate basic mechanisms of learning (Shahaf and Marom, 2001;le Feber et al, 2010). Closed-loop experiments are also relevant to the technology of neural interfaces (MussaIvaldi and Miller, 2003;Nicolelis, 2003).…”