Principles for coding associative memories in a compact neural network

Molecular Systems Biology

Yellinek

Itskovits

et al. 2022

Self Cite

Efficient navigation based on chemical cues is an essential feature shared by all animals. These cues may be encountered in complex spatiotemporal patterns and with orders of magnitude varying intensities. Nevertheless, sensory neurons accurately extract the relevant information from such perplexing signals. Here, we show how a single sensory neuron in Caenorhabditis elegans animals can cell‐autonomously encode complex stimulus patterns composed of instantaneous sharp changes and of slowly changing continuous gradients. This encoding relies on a simple negative feedback in the G‐protein‐coupled receptor (GPCR) signaling pathway in which TAX‐6/Calcineurin plays a key role in mediating the feedback inhibition. This negative feedback supports several important coding features that underlie an efficient navigation strategy, including exact adaptation and adaptation to the magnitude of the gradient's first derivative. A simple mathematical model explains the fine neural dynamics of both wild‐type and tax‐6 mutant animals, further highlighting how the calcium‐dependent activity of TAX‐6/Calcineurin dictates GPCR inhibition and response dynamics. As GPCRs are ubiquitously expressed in all sensory neurons, this mechanism may be a general solution for efficient cell‐autonomous coding of external stimuli.

Section: Discussionsupporting

confidence: 91%

A negative feedback loop in the GPCR pathway underlies efficient coding of external stimuli

Molecular Systems Biology

Yellinek

Itskovits

et al. 2022

Self Cite

“…Classical experiments demonstrated that local synaptic activity marks (“captures”) the synapse and targets it with proteins that support long-term plasticity in a process known as synaptic tagging and capture ( 49 , 50 ). As C. elegans animals can form various memory types, ranging from simple nonassociative habituation to classical conditioned associative memories ( 51 – 54 ), local synaptic activity can efficiently mark specific target synapses. Such selected tagging increases the memory capacity of the network as neurons can be partitioned into distinct synaptic modules, each undergoing plasticity processes independently of the others.…”

Section: Discussionmentioning

confidence: 99%

The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations

Proc. Natl. Acad. Sci. U.S.A.

Ratner

Emmons

et al. 2023

Self Cite

Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.

“…Classical experiments demonstrated that local synaptic activity marks ('captures') the synapse and targets it with proteins that support long-term plasticity in a process known as synaptic tagging and capture 47,48 . As C. elegans animals can form various memory types, ranging from simple non-associative habituation to classical conditioned associative memories [49][50][51][52] , local synaptic activity can efficiently mark specific target synapses. Such selected tagging increases the memory capacity of the network as neurons can be partitioned into distinct synaptic modules, each undergoing plasticity processes independently of the others.…”

Section: Discussionmentioning

confidence: 99%

The synaptic organization in the C. elegans neural network suggests significant local compartmentalized computations

Ratner

Emmons

et al. 2022

Preprint

Self Cite

Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear if simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of C. elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed, nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually-synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the non-random synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally-related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network presumably to facilitate distinct parallel functions along a single neurite, effectively increasing the computational capacity of the neural network.