Universität zu Köln

Seeholzer, Alexander, Deger, Moritz ORCID: 0000-0002-2775-2611 and Gerstner, Wulfram (2019). Stability of working memory in continuous attractor networks under the control of short-term plasticity. PLoS Comput. Biol., 15 (4). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1553-7358

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Abstract

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory. Author summary The ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby pinning down memorized positions. Here, we present a general theory that accurately predicts the extent of this pinning down by short-term plasticity in a broad class of biologically plausible network models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a novel theoretical link from the microscopic substrate of working memoryneurons and synaptic connectionsto observable behavioral correlates, for example the susceptibility to distracting stimuli.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Seeholzer, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Deger, Moritz UNSPECIFIED orcid.org/0000-0002-2775-2611 UNSPECIFIED Gerstner, Wulfram UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-151654
DOI:	10.1371/journal.pcbi.1006928
Journal or Publication Title:	PLoS Comput. Biol.
Volume:	15
Number:	4
Date:	2019
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1553-7358
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language RAT PREFRONTAL CORTEX; NEURONAL FIRING RATES; NEURAL CIRCUIT BASIS; PERSISTENT ACTIVITY; PATH-INTEGRATION; GRID CELLS; DYNAMICS; MODEL; NEUROMODULATION; ORIENTATION Multiple languages Biochemical Research Methods; Mathematical & Computational Biology Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15165