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Intracellular dynamics in cuneate nucleus neurons support self-stabilizing learning of generalizable tactile representations

Rongala, Udaya B. ; Spanne, Anton LU ; Mazzoni, Alberto ; Bengtsson, Fredrik LU ; Oddo, Calogero M. and Jörntell, Henrik LU (2018) In Frontiers in Cellular Neuroscience 12.
Abstract

How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip... (More)

How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cuneate nucleus, Intrinsic dynamics, Neuronal plasticity, Neurophysiology, Synaptic integration, Tactile, Touch
in
Frontiers in Cellular Neuroscience
volume
12
article number
210
publisher
Frontiers
external identifiers
  • pmid:30108485
  • scopus:85053302252
ISSN
1662-5102
DOI
10.3389/fncel.2018.00210
language
English
LU publication?
yes
id
ab97c91d-62a4-406c-8f47-48dc3e3e5445
date added to LUP
2018-10-18 14:17:57
date last changed
2020-07-29 05:24:30
@article{ab97c91d-62a4-406c-8f47-48dc3e3e5445,
  abstract     = {<p>How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.</p>},
  author       = {Rongala, Udaya B. and Spanne, Anton and Mazzoni, Alberto and Bengtsson, Fredrik and Oddo, Calogero M. and Jörntell, Henrik},
  issn         = {1662-5102},
  language     = {eng},
  month        = {07},
  publisher    = {Frontiers},
  series       = {Frontiers in Cellular Neuroscience},
  title        = {Intracellular dynamics in cuneate nucleus neurons support self-stabilizing learning of generalizable tactile representations},
  url          = {http://dx.doi.org/10.3389/fncel.2018.00210},
  doi          = {10.3389/fncel.2018.00210},
  volume       = {12},
  year         = {2018},
}