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

Rongala, Udaya B. (författare)
Sant'Anna School of Advanced Studies
Spanne, Anton (författare)
Lund University,Lunds universitet,Hjärnans sensorimotoriska funktioner,Forskargrupper vid Lunds universitet,Neural Basis of Sensorimotor Control,Lund University Research Groups
Mazzoni, Alberto (författare)
Sant'Anna School of Advanced Studies
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Bengtsson, Fredrik (författare)
Lund University,Lunds universitet,Hjärnans sensorimotoriska funktioner,Forskargrupper vid Lunds universitet,Neural Basis of Sensorimotor Control,Lund University Research Groups
Oddo, Calogero M. (författare)
Sant'Anna School of Advanced Studies
Jörntell, Henrik (författare)
Lund University,Lunds universitet,Hjärnans sensorimotoriska funktioner,Forskargrupper vid Lunds universitet,Neural Basis of Sensorimotor Control,Lund University Research Groups
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 (creator_code:org_t)
2018-07-31
2018
Engelska.
Ingår i: Frontiers in Cellular Neuroscience. - : Frontiers Media SA. - 1662-5102. ; 12
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)

Nyckelord

Cuneate nucleus
Intrinsic dynamics
Neuronal plasticity
Neurophysiology
Synaptic integration
Tactile
Touch

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