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Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Panzeri S, Safaai H, De Feo V, Vato A - Front Neurosci (2016)

Bottom Line: Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue.Yet, current BMIs can exchange relatively small amounts of information with the brain.We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain.

View Article: PubMed Central - PubMed

Affiliation: Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy.

ABSTRACT
Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

No MeSH data available.


Related in: MedlinePlus

Schematic of a bidirectional brain-machine interface. A bidirectional BMI has two pathways of communication with the brain: an afferent pathway from some sensors to the brain and an efferent pathway from the brain to a device controlled by it. The decoder—or motor interface—transforms the recorded activity into motor commands for the device. The encoder—or sensory interface—transmits the information about the external world or about the state of the device to the brain by delivering electrical stimulation patterns to it.
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Figure 1: Schematic of a bidirectional brain-machine interface. A bidirectional BMI has two pathways of communication with the brain: an afferent pathway from some sensors to the brain and an efferent pathway from the brain to a device controlled by it. The decoder—or motor interface—transforms the recorded activity into motor commands for the device. The encoder—or sensory interface—transmits the information about the external world or about the state of the device to the brain by delivering electrical stimulation patterns to it.

Mentions: Researchers have also developed bidirectional BMIs (Figure 1) in which both a decoder of motor intention and an encoder of sensory information exchange information with the brain in a closed-loop (Reger et al., 2000; Nicolelis, 2003; Lebedev and Nicolelis, 2006; Nicolelis and Lebedev, 2009; O'Doherty et al., 2009, 2011; Mussa-Ivaldi et al., 2010; Lebedev et al., 2011; Carmena, 2013; Moxon and Foffani, 2015). Such systems may have important clinical applications because (unlike motor BMIs) they can provide the brain with non-visual feedback information (such as tactile or proprioceptive information) that is important for compliant task execution. Bidirectional BMIs may also help to automatically execute tasks without focusing attention on each single motor command. Recently we proposed to achieve this goal through a class of bidirectional BMIs, implemented both in anesthetized and awake rodents (Vato et al., 2012, 2014; Boi et al., 2015), in which the decoding and encoding interfaces generated a motor program similar to the force fields generated by the spinal cord when combining motor and sensory information (Shadmehr et al., 1993; Mussa-Ivaldi et al., 1994).


Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Panzeri S, Safaai H, De Feo V, Vato A - Front Neurosci (2016)

Schematic of a bidirectional brain-machine interface. A bidirectional BMI has two pathways of communication with the brain: an afferent pathway from some sensors to the brain and an efferent pathway from the brain to a device controlled by it. The decoder—or motor interface—transforms the recorded activity into motor commands for the device. The encoder—or sensory interface—transmits the information about the external world or about the state of the device to the brain by delivering electrical stimulation patterns to it.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837323&req=5

Figure 1: Schematic of a bidirectional brain-machine interface. A bidirectional BMI has two pathways of communication with the brain: an afferent pathway from some sensors to the brain and an efferent pathway from the brain to a device controlled by it. The decoder—or motor interface—transforms the recorded activity into motor commands for the device. The encoder—or sensory interface—transmits the information about the external world or about the state of the device to the brain by delivering electrical stimulation patterns to it.
Mentions: Researchers have also developed bidirectional BMIs (Figure 1) in which both a decoder of motor intention and an encoder of sensory information exchange information with the brain in a closed-loop (Reger et al., 2000; Nicolelis, 2003; Lebedev and Nicolelis, 2006; Nicolelis and Lebedev, 2009; O'Doherty et al., 2009, 2011; Mussa-Ivaldi et al., 2010; Lebedev et al., 2011; Carmena, 2013; Moxon and Foffani, 2015). Such systems may have important clinical applications because (unlike motor BMIs) they can provide the brain with non-visual feedback information (such as tactile or proprioceptive information) that is important for compliant task execution. Bidirectional BMIs may also help to automatically execute tasks without focusing attention on each single motor command. Recently we proposed to achieve this goal through a class of bidirectional BMIs, implemented both in anesthetized and awake rodents (Vato et al., 2012, 2014; Boi et al., 2015), in which the decoding and encoding interfaces generated a motor program similar to the force fields generated by the spinal cord when combining motor and sensory information (Shadmehr et al., 1993; Mussa-Ivaldi et al., 1994).

Bottom Line: Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue.Yet, current BMIs can exchange relatively small amounts of information with the brain.We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain.

View Article: PubMed Central - PubMed

Affiliation: Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy.

ABSTRACT
Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

No MeSH data available.


Related in: MedlinePlus