Summary: A recent study demonstrates that the brain brain continuously uses its “memory machine” to enhance its forecasts for the future. By imaging the auditory brain of mice, scientists found that cells track sensory sources over period, forming “echoes” that help identify new knowledge.
A neural network model replicated these studies, showing that the body’s cable normally supports innovation recognition. This work is critical for belief, learning, and decision-making and perhaps explain why individuals with schizophrenia struggle to identify fresh from outdated information.
The results highlight the role of neural systems, more than one neurons, in innovation recognition. This study advances our understanding of both regular mental functions and problems involving incorrect prediction control.
Major Information
- Neurological Echoes: The brain tracks sensory type using “echoes” of previous stimuli, which help kind short-term memories.
- Automatic Novelty Detection: Neurological network, rather than specific cells, normally detect novel stimuli based on their cable.
- Schizophrenia Insight: The findings may explain why people with schizophrenia struggle to distinguish between fresh and outdated information.
Source: Columbia University
The cerebral cortex is the largest part of a mammal’s brain, and by some measures the most important. In humans in particular, it’s where most things happen—like perception, thinking, memory storage and decision-making.
According to one recent theory, the cortex’s main function is to identify and encode new information that it receives from the outside world and compare it to what was anticipated to happen.
A new study published today in the journal , Neuron , takes a big step toward proving that hypothesis. The paper’s lead author is Yuriy Shymkiv, a postdoctoral fellow in the , lab of Professor Rafael Yuste.
” We found that the cortex acts like a memory machine, encoding new experiences, and predicting the very near future”, Shymkiv said.
This study provides” a great deal of insight into the function of the cerebral cortex,” Yuste said, noting that it also aids in clarifying crucial processes in the normal brain.
” Novelty is the distinction between what you anticipated will happen and what actually transpired.” This study demonstrates that the cerebral cortex is constantly detecting novel stimuli to alter and enhance its future predictions. Novelty detection is a crucial role for people and other animals.
The research team conducted a study to understand how mice responded to a mix of both well-known and new sensory stimuli. The experiment’s stimuli were different pitches of sounds played.
After examining the auditory cortex of mice, which controls sound, groups of neurons responded to both how novel the sound was and the amount of time it was played.
Intriguingly, they found that each sound left a trail of neuronal activity, which they refer to as an “echo”, which tracked sensory inputs over time, and formed short-term memories of recent inputs.
These activity echoes not only ensured that each incoming stimulus produced a distinct response, but they also helped to select newly created stimuli, which helped to make those responses much more powerful.
The team created a neural network model of the auditory cortex to improve its ability to recognize fresh stimuli in order to further understand these findings. It replicated what that they had seen in mice, showing that networks of neurons also used activity “echoes” to store a model of the environment, and used it to detect change. They came to the conclusion that novelty detection is an automatic emergent characteristic of the network because of the way the cortex is wired, with loops of connected neurons.
” This is a leap forward in understanding how the brain does such a good job of detecting novelty”, said Yuste, noting that the model that Shymkiv created builds on the ideas of John Hopfield, who , won the Nobel Prize , last year for building neural network models and pioneering artificial intelligence.
Additionally, the study provides fresh information about the primary function of the cerebral cortex in schizophrenia. Clinicians have been aware for a long time that those who suffer from schizophrenia are unable to tell new information from outdated information.
Scientists attempted to explain those findings by interpreting the behavior of individual neurons, but they ran into difficulties. One of the key findings of this paper is the finding that novelty detection isn’t the product of single neurons but rather of neural networks.
We’re very excited that these findings will help us understand this crucial area of the brain and provide valuable insight into situations where those functions malfunction and how to fix them, Yuste said.
About this news about neuroscience research
Author: Christopher Shea
Source: Columbia University
Contact: Christopher Shea – Columbia University
Image: The image is credited to Neuroscience News
Original Research: Open access.
By Yuriy Shymkiv and al.,” Slow cortical dynamics generate context processing and novelty detection.” Neuron
Abstract
Temporal cortical dynamics lead to novelty detection and context processing.
The cortex amplifies responses to novel stimuli while reducing previously used ones. It is also altered by schizophrenia to effectively process sensory information and create predictive models of the environment.
We used an auditory “oddball” paradigm and two-photon calcium imaging to measure responses to simple and complex stimuli across the auditory cortex of mice to investigate the circuit mechanisms underlying novelty detection. Statistics and complexity from stimulus generated specific responses across auditory thresholds.
Neuronal ensembles consistently encoded temporal and auditory characteristics. Interestingly, stimulus-evoked population responses were particularly long lasting, reflecting stimulus history and affecting future responses.
These slow cortical dynamics created stronger responses to novel stimuli and encoded stimulus temporal context.
Slow network dynamics and biological data were captured by repeating neural network models trained on the oddball task.
We come to the conclusion that processing and novelty detection are influenced by the slow dynamics of recurrent cortical networks.
