Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

Abstract

Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions. While this might seem to foretell a new generation of AI approaches arising from a deeper exploration of neuroscience specifically for AI, the most direct path for achieving this is unclear. Here we discuss cultural differences between the two fields, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI. For example, the two fields feed two very different applications that at times require potentially conflicting perspectives. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.

A survey of brain network analysis by electroencephalographic signals

“…The brain intelligence model is proposed to extend and advance contemporary AI in the light of human memory function (Lu et al 2018). Additionally, the big gap between AI and neuroscience is the culture to communicate with each other which will be solved to extend them further (Chance et al 2020). More reviews and reports remark and analyze that AI has been developing from brain science and also expedites it (Fan et al 2020;Hassabis et al 2017;Savage 2019;Shapshak 2018).…”

Psychiatrie im dritten Jahrzehnt des 21. Jahrhunderts

“…Dies ist wichtig, wenn es beispielsweise um die Triage geht, d. h. um die Optimierung des Outcomes bei der Allokation knapper Ressourcen: Wenn nach einer Massenkarambolage 7 Schwerverletzte zugleich eintreffen, aber nur 4 Intensivbetten zur Verfügung stehen, sollte die Entscheidung, wer behandelt wird und wer nicht, nach rationalen Gesichtspunkten getroffen werden. 6 Gelöst wird es in aller Welt nach den gleichen Prinzipien. Wenn an der Lösung künstliche Intelligenz beteiligt ist (kein Mensch kann so viele Daten wie neuronale Netzwerke verarbeiten und dies ohne affektiv getönte und wertende (Vor-)Urteile), sagen die Amerikaner "der Computer entscheidet", wohingegen man in Europa eher sagt, "dass wir entscheiden, uns aber vom Computer unterstützen lassen".…”

The Identification of Latent Atrial Fibrillation, Decreased Ejection Fraction of the Left Ventricle and Potassium Metabolism Disorders on the Conventional Electrocardiogram Using Deep Convolutional Neural Network as an Element of Artificial Intelligence

“…Применение ИНС в здравоохранении включает клиническую диагностику, прогнозирование, распознавание речи, анализ и интерпретацию изображений, автоматическую интерпретацию электрокардиограммы (ЭКГ) и разработку лекарств [5]. Первоначально математически описанные приемы обработки информации, осуществляемые биологическими нервными клетками, были использованы для создания структурных элементов ИНС, которые концептуально используются в нейробиологии и принадлежат к классу статистических процедур, раскрывают более сложные ассоциации, чем математические уравнения [6].…”