Characterizing Sensory Re-weighting for Human Postural Control

In order to survive in the wide range of sensory contexts that comprise our physical world, the nervous system employs adaptive mechanisms that optimize functional behaviors within a given sensory environment. Human bipedal stance control requires that the nervous system obtain relevant information about the environment and the body's relationship with it from multiple sensory systems. How does the nervous system accomplish this when the sensory environment compromises the information available from a given sensory system? In previous theoretical and empirical work, we have provided evidence of nonlinearities that are consistent with an hypothesis of sensory re-weighting: The nervous system adapts to changing sensory contexts by decreasing its dependence, or weighting, on the compromised system and increases its weighting of other inputs. This thesis presents empirical findings that further support the sensory re-weighting hypothesis and further efforts towards characterizing sensory re-weighting by providing empirical results that provide important constraints on any proposed sensory re-weighting scheme. First, postural responses to complex visual motion consisting of the sum of 10 different sinusoidal components, were measured at two different amplitudes. Changes in the gain of body sway to visual motion were consistent with the nonlinearities previously interpreted as evidence for sensory re-weighting. Further, the observed changes in gain did not vary significantly as a function of stimulus frequency. Second, we found evidence indicating a temporal asymmetry in the sensory re-weighting process dependent upon the direction of the change in stimulus motion amplitude: the change in postural response is faster to a rapid increase versus decrease in stimulus amplitude. This temporal asymmetry was interpreted functionally: an increase in visual environmental motion may threaten balance, requiring a rapid down-weighting of vision if a strong dependence upon visual information would increase postural response beyond the stability boundaries of stance. Conversely, if stance is already stable in the face of large visual motion amplitude, a decrease in motion amplitude does not threaten balance and adapting rapidly to the new sensory conditions is not critical to avoid falling

Position and Velocity Coupling of Postural Sway to Somatosensory Drive

, and sinusoidal vertical axis rotation (SVAR) technique rotates Elaine Henson. Position and velocity coupling of postural sway seated subjects at a range of frequencies to measure the gain to somatosensory drive. J. Neurophysiol. 79: 1661Neurophysiol. 79: -1674Neurophysiol. 79: , 1998. and phase of eye movements in the dark as a measure of Light touch contact of a fingertip to a stationary surface provides vestibular function (Howard 1982; whole-body posture body sway relative to the touch plate averaged 20-30Њ at 0.1-Hz Such control theory techniques have not been impledrive and decreased approximately linearly to 0130Њ at 0.8-Hz drive. System gain was Ç1 across frequency. The large phase lags mented for somatosensory function with regard to upright observed cannot be accounted for with velocity coupling alone but stance control. Extensive empiric studies have determined indicate that body sway also was coupled to the position of the that the primary role of somatosensation is to provide infortouch plate. Fitting of a linear second-order model to the data mation concerning contact surface forces and properties such suggests that postural control parameters are not fixed but adapt as texture and friction and the relative configuration of body to the moving frame of reference. Moreover, coupling to both segments (Dietz 1992

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons

Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a “hot zone” in distal apical dendrite and apical tuft where both high- and low-threshold Ca2+ ionic conductances had densities 1–2 orders of magnitude higher than anywhere else in the apical dendrite. We simulated injection of broad spectrum sinusoidal currents with linearly increasing frequency to calculate the input impedance of individual compartments, the transfer impedance between the soma and key compartments within the dendritic tree, and a dimensionless term we introduce called resonance quality. We show that input resonance analysis distinguished at least four distinct zones within the model based on properties of their frequency preferences: basal dendrite which displayed little resonance; soma/proximal apical dendrite which displayed resonance at 5–23 Hz, strongest at 5–10 Hz and hyperpolarized/resting membrane potentials; distal apical dendrite which displayed resonance at 8–19 Hz, strongest at 10 Hz and depolarized membrane potentials; and apical tuft which displayed a weak resonance largely between 8 and 10 Hz across a wide range of membrane potentials. Transfer resonance analysis revealed that changes in subthreshold electrical coupling were found to modulate the transfer resonant frequency of signals transmitted from distal apical dendrite and apical tuft to the soma, which would impact the frequencies that individual neurons are expected to respond to and reinforce. Furthermore, eliminating the hot zone was found to reduce amplification of resonance within the model, which contributes to reduced excitability when perisomatic and distal apical regions receive coincident stimulating current injections. These results indicate that the interactions between different functional zones should be considered in a more complete understanding of neuronal integration. Resonance analysis may therefore be a useful tool for assessing the integration of inputs across the entire neuronal membrane

Data_Sheet_1_Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons.DOCX

<p>Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a “hot zone” in distal apical dendrite and apical tuft where both high- and low-threshold Ca²⁺ ionic conductances had densities 1–2 orders of magnitude higher than anywhere else in the apical dendrite. We simulated injection of broad spectrum sinusoidal currents with linearly increasing frequency to calculate the input impedance of individual compartments, the transfer impedance between the soma and key compartments within the dendritic tree, and a dimensionless term we introduce called resonance quality. We show that input resonance analysis distinguished at least four distinct zones within the model based on properties of their frequency preferences: basal dendrite which displayed little resonance; soma/proximal apical dendrite which displayed resonance at 5–23 Hz, strongest at 5–10 Hz and hyperpolarized/resting membrane potentials; distal apical dendrite which displayed resonance at 8–19 Hz, strongest at 10 Hz and depolarized membrane potentials; and apical tuft which displayed a weak resonance largely between 8 and 10 Hz across a wide range of membrane potentials. Transfer resonance analysis revealed that changes in subthreshold electrical coupling were found to modulate the transfer resonant frequency of signals transmitted from distal apical dendrite and apical tuft to the soma, which would impact the frequencies that individual neurons are expected to respond to and reinforce. Furthermore, eliminating the hot zone was found to reduce amplification of resonance within the model, which contributes to reduced excitability when perisomatic and distal apical regions receive coincident stimulating current injections. These results indicate that the interactions between different functional zones should be considered in a more complete understanding of neuronal integration. Resonance analysis may therefore be a useful tool for assessing the integration of inputs across the entire neuronal membrane.</p

Data_Sheet_2_Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons.XLSX

<p>Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a “hot zone” in distal apical dendrite and apical tuft where both high- and low-threshold Ca²⁺ ionic conductances had densities 1–2 orders of magnitude higher than anywhere else in the apical dendrite. We simulated injection of broad spectrum sinusoidal currents with linearly increasing frequency to calculate the input impedance of individual compartments, the transfer impedance between the soma and key compartments within the dendritic tree, and a dimensionless term we introduce called resonance quality. We show that input resonance analysis distinguished at least four distinct zones within the model based on properties of their frequency preferences: basal dendrite which displayed little resonance; soma/proximal apical dendrite which displayed resonance at 5–23 Hz, strongest at 5–10 Hz and hyperpolarized/resting membrane potentials; distal apical dendrite which displayed resonance at 8–19 Hz, strongest at 10 Hz and depolarized membrane potentials; and apical tuft which displayed a weak resonance largely between 8 and 10 Hz across a wide range of membrane potentials. Transfer resonance analysis revealed that changes in subthreshold electrical coupling were found to modulate the transfer resonant frequency of signals transmitted from distal apical dendrite and apical tuft to the soma, which would impact the frequencies that individual neurons are expected to respond to and reinforce. Furthermore, eliminating the hot zone was found to reduce amplification of resonance within the model, which contributes to reduced excitability when perisomatic and distal apical regions receive coincident stimulating current injections. These results indicate that the interactions between different functional zones should be considered in a more complete understanding of neuronal integration. Resonance analysis may therefore be a useful tool for assessing the integration of inputs across the entire neuronal membrane.</p