Adaptation in single neurons provides memory for language processing


Date:
August 12, 2020
Source:
Max Planck Institute for Psycholinguistics
Summary:
To understand language, we have to remember the words that
were uttered and combine them into an interpretation. How
does the brain retain information long enough to accomplish
this, despite the fact that neuronal firing events are very
short-lived? Researchers propose a neurobiological explanation
bridging this discrepancy. Neurons change their spike rate based
on experience and this adaptation provides memory for sentence
processing.



FULL STORY ==========================================================================
Did the man bite the dog, or was it the other way around? When
processing an utterance, words need to be assembled into the correct interpretation within working memory. One aspect of comprehension
is to establish 'who did what to whom'. This process of unification
takes much longer than basic events in neurobiology, like neuronal
spikes or synaptic signaling. Hartmut Fitz, lead investigator at the Neurocomputational Models of Language group at the Max Planck Institute
for Psycholinguistics, and his colleagues propose an account where
adaptive features of single neurons supply memory that is sufficiently long-lived to bridge this temporal gap and support language processing.


========================================================================== Model comparisons Together with researchers Marvin Uhlmann, Dick van den
Broek, Peter Hagoort, Karl Magnus Petersson (all Max Planck Institute
for Psycholinguistics) and Renato Duarte (Ju"lich Research Centre,
Germany), Fitz studied working memory in spiking networks through an
innovative combination of experimental language research with methods
from computational neuroscience.

In a sentence comprehension task, circuits of biological neurons and
synapses were exposed to sequential language input which they had
to map onto semantic relations that characterize the meaning of an
utterance. For example, 'the cat chases a dog' means something different
than 'the cat is chased by a dog' even though both sentences contain
similar words. The various cues to meaning need to be integrated within
working memory to derive the correct message. The researchers varied
the neurobiological features in computationally simulated networks and
compared the performance of different versions of the model. This allowed
them to pinpoint which of these features implemented the memory capacity required for sentence comprehension.

Towards a computational neurobiology of language They found that working
memory for language processing can be provided by the down-regulation of neuronal excitability in response to external input. "This suggests that working memory could reside within single neurons, which contrasts with
other theories where memory is either due to short-term synaptic changes
or arises from network connectivity and excitatory feedback," says Fitz.

Their model shows that this neuronal memory is context-dependent,
and sensitive to serial order which makes it ideally suitable for
language. Additionally, the model was able to establish binding relations between words and semantic roles with high accuracy.

"It is crucial to try and build language models that are directly grounded
in basic neurobiological principles," declares Fitz. "This work shows
that we can meaningfully study language at the neurobiological level
of explanation, using a causal modelling approach that may eventually
allow us to develop a computational neurobiology of language."

========================================================================== Story Source: Materials provided by
Max_Planck_Institute_for_Psycholinguistics. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Hartmut Fitz, Marvin Uhlmann, Dick van den Broek, Renato Duarte,
Peter
Hagoort, Karl Magnus Petersson. Neuronal spike-rate adaptation
supports working memory in language processing. Proceedings
of the National Academy of Sciences, 2020; 202000222 DOI:
10.1073/pnas.2000222117 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200812144024.htm

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