Neuronal heterogeneity

Recent progress in artificial intelligence (AI), particularly through the development of artificial neural networks (ANNs), has significantly benefited from insights gained from neuroscience. This progress has enhanced the replication of complex cognitive tasks such as vision and natural language processing (1,2). However, ANNs still struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly (3–8). Neuromodulators, the chemical messengers that target neurons and synapses in specific brain regions,

Neurons in the brain exhibit significant heterogeneity in their dynamics. Whether this diversity plays any computational role and if it is exploited to perform efficient computation is not well understood. We used the reservoir computing paradigm to answer this question. Balanced networks with different levels of heterogeneity in their neuronal time constants were set up and benchmarked against various working-memory-related tasks, including stimulus recall, forecasting, and nonlinear transformation. Our results suggest that neuronal heterogeneity, even without any recurrent communication, si

Visual search involves scanning the surroundings to find objects of interest amidst a clutter of irrelevant distractors. A commonly accepted theory proposes that pop out visual search is governed by a winner-take-all (WTA) competition between contextually modulated cells that form a saliency map. Yet, research indicates that WTA's capacity to gather information even from large populations, has limitations, casting doubt on its role in pop out visual search. In our investigation, we performed a modeling study to assess the accuracy of WTA in detecting the distinctive object in a pop out task. W

…as decreases in cell and circuit heterogeneity. In recent experimental work, we demonstrated that biophysical diversity is a key characteristic of human cortical pyramidal cells, and past theoretical work has shown that neuronal heterogeneity improves a neural circuit’s ability to encode information. Viewed alongside the fact that seizure is an information-poor brain state, these findings motivate the hypothe