Hopfield networks are important for modeling brain processes, but they have powerful other uses too.

“You should have individual synapses, but you should also have some dendritic trees and some astrocytes.” “It turns out you actually need a combination of features in some balance,” says Mr. They estimate it works more than doubly as well as a traditional Hopfield network. The researchers found that a network containing a mix of both pairwise and set-wise connections performed best and retained the highest number of memories. He created a modified Hopfield network in which not just pairs of neurons but sets of three, four, or more neurons could link up too, such as might occur in the brain through astrocytes and dendritic trees.Īlthough the new network allowed these so-called “set-wise” connections, overall it contained the same total number of connections as before. “It’s simply not realistic that only pairwise connections between neurons exist in the brain,” explains Mr. Additionally, connections between neurons are modulated by other cell types called astrocytes. But in reality, neurons have intricate branched structures called dendrites that provide multiple points for connection, so the brain relies on a much more complex arrangement of synapses to get its cognitive jobs done. Pairwise connections represent how two neurons connect at a synapse, a connection point between two neurons in the brain. In classical Hopfield networks, however, neurons in the model reciprocally connect to other neurons in the network to form a series of what are called “pairwise” connections. The network is “trained” to encode these patterns, then researchers can test its memory of them by presenting a series of blurry or incomplete patterns and seeing if the network can recognize them as one it already knows. Hopfield networks store memories as patterns of weighted connections between different neurons in the system. Burns and Professor Fukai, sets of three or more neurons can connect simultaneously.

In the classical Hopfield network (left), each neuron (I, j, k, l) is connected to the others in a pairwise manner. The alteration has resulted in a network that not only better mirrors the way neurons and other cells are connected in the brain, but also has the capacity to store significantly more memories. Researchers at the Okinawa Institute of Science and Technology (OIST) have made improvements to a widely utilized computer model of memory, known as a Hopfield network, by incorporating insights from biology. Despite this, encoding the complex biology of the brain into a computer model for further study has proven to be a difficult task due to the limited understanding of the underlying biology of the brain. The intricate interplay of electrical and biochemical signals, as well as the web of connections between neurons and other cell types, creates the infrastructure for memories to be formed. However, constructing such models is a delicate task. This was achieved by modifying a classical neural network.Ĭomputer models play a crucial role in investigating the brain’s process of making and retaining memories and other intricate information. Researchers have developed a new model inspired by recent biological discoveries that shows enhanced memory performance. The researchers discovered that a network that incorporated both pairwise and set-wise connections performed best and retained the greatest number of memories.

Fukai mori