Training biological neural cultures: Towards Hebbian learning

Vicente, José Manuel Ferrández; Lorente, V.; Paz, Félix de la; Fernández, Eduardo

doi:10.1016/j.neucom.2012.09.031

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Here, representative paradigms for ES and their functions are listed with key parameters (Table 2 ). Special attention should be given to stimulus paradigms designed based on Hebbian theory 39 and the free energy principle 102 , which evoke advanced functions such as learning and memory and make full use of exquisitely connected natural neural networks without affecting internal development.…”

Section: Bidirectional Communications With Neural Network On Measmentioning

confidence: 99%

Nanomaterial-based microelectrode arrays for in vitro bidirectional brain–computer interfaces: a review

Liu

Yang

et al. 2023

Microsyst Nanoeng

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A bidirectional in vitro brain–computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.

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Section: Bidirectional Communications With Neural Network On Measmentioning

confidence: 99%

Nanomaterial-based microelectrode arrays for in vitro bidirectional brain–computer interfaces: a review

Liu

Yang

et al. 2023

Microsyst Nanoeng

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“…In recent years, researchers have focused on the development of new learning mechanisms based on neural interactions in the nervous system [16]. Hebbian learning and subsequently spike-timing-dependent plasticity (STDP) learning mechanisms have been favored based on their hypothesized biological evidence for learning in the nervous system and became a key feature of training procedures used in spiking neural networks [41][42]. Although, spike-based back-propagation [43] and deep networks transformed into spiking deep networks [44] have been introduced, they rely on supervised…”

Section: Learning Mechanism (Btdp)mentioning

confidence: 99%

A novel un-supervised burst time dependent plasticity learning approach for biologically pattern recognition networks

Amiri

Jafari‬

Makkiabadi

et al. 2023

Information Sciences

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“…In this regard, based on biological observation, Spatial-STDP affects the transmissions of AMPA and GABA neurotransmitters during the learning process. Similar to Hebbian learning 30 , 31 , STDP and spatial-STDP are learning approach based on biological evidence of nervous system learning. In fact, in spatial-STDP learning procedure, the synaptic weights are updated based on the spike activity of the presynaptic and postsynaptic neurons and the distance between them, and this process continues until the network learn the patterns.…”

Section: Architecture and Learning Mechanism Of The Pattern Recogniti...mentioning

confidence: 99%

Recognizing intertwined patterns using a network of spiking pattern recognition platforms

Amiri

Jafari‬

Makkiabadi

et al. 2022

Sci Rep

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Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. Here, the concentration is on improvement the cognitive potential of artificial intelligence network with a bio-inspired structure. In this regard, four spiking pattern recognition platforms for recognizing digits and letters of EMNIST, patterns of YALE, and ORL datasets are proposed. All networks are developed based on a similar structure in the input image coding, model of neurons (pyramidal neurons and interneurons) and synapses (excitatory AMPA and inhibitory GABA currents), and learning procedure. Networks 1–4 are trained on Digits, Letters, faces of YALE and ORL, respectively, with the proposed un-supervised, spatial–temporal, and sparse spike-based learning mechanism based on the biological observation of the brain learning. When the networks have reached the highest recognition accuracy in the relevant patterns, the main goal of the article, which is to achieve high-performance pattern recognition system with higher cognitive ability, is followed. The pattern recognition network that is able to detect the combination of multiple patterns which called intertwined patterns has not been discussed yet. Therefore, by integrating four trained spiking pattern recognition platforms in one system configuration, we are able to recognize intertwined patterns. These results are presented for the first time and could be the pioneer of a new generation of pattern recognition networks with a significant ability in smart machines.

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Training biological neural cultures: Towards Hebbian learning

Cited by 11 publications

References 13 publications

Nanomaterial-based microelectrode arrays for in vitro bidirectional brain–computer interfaces: a review

Nanomaterial-based microelectrode arrays for in vitro bidirectional brain–computer interfaces: a review

A novel un-supervised burst time dependent plasticity learning approach for biologically pattern recognition networks

Recognizing intertwined patterns using a network of spiking pattern recognition platforms

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