A System for Neural Recording and Closed-Loop Intracortical Microstimulation in Awake Rodents

Venkatraman, Subramaniam; Elkabany, K.; Long, John D.; Yao, Yimin; Carmena, Jose M.

doi:10.1109/tbme.2008.2005944

Cited by 86 publications

(58 citation statements)

References 39 publications

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“…Furthermore, it is known that the behavioral modification of S1bf to sensory afferents persists even after transection of the infraorbital nerve (Hentschke et al 2006), suggesting that this modulation is generated by the brain and not induced by stimuli. Therefore, the use of ICMS to bypass the sensory periphery was insufficient to address this issue, although, while it has been found that ICMS-evoked oscillations in S1bf vary as a function of behavior (Venkatraman and Carmena 2009), this modulation was observed to occur after the positive and negative features described in this work, during the return to baseline excitation. Notably, we did not observe any anticipatory increase in firing rate over the course of a trial (Fig.…”

Section: Discussionmentioning

confidence: 64%

“…The current level used for ICMS conditioning was set at 5 A below the smallest current level that evoked a twitch, observable in the accelerometer data. It has been shown that with these materials and stimulation parameters it is possible to record the initial excitatory neural response to ICMS (Butovas and Schwarz 2003;Venkatraman et al 2009) as well as drive behavior (Butovas and Schwarz 2007).…”

Section: Microstimulationmentioning

confidence: 99%

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Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus

Long

Carmena

2013

Journal of Neurophysiology

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Long JD 2nd, Carmena JM. Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus. J Neurophysiol 109: 2585-2595, 2013. First published March 6, 2013 doi:10.1152/jn.00553.2012.-The rodent somatosensory barrel cortex (S1bf) has proved a valuable model for studying neural plasticity in vivo. It has been observed that sensory deprivation or conditioning reorganizes sensory-driven activity within S1bf. These observations suggest a role for S1bf in somatosensory learning. This study evaluated the hypothesis that the response properties of extracellularly recorded neurons in S1bf would change as subjects learned to respond to stimulation of S1bf. Intracortical microstimulation (ICMS) of S1bf was used as a means for bypassing feedforward drive from the sensory periphery, midbrain, and thalamus while exciting local cortical networks. To separate the learning of this conditioned stimulus-conditioned response (CS-CR) from other elements of the task, we employed a cross-modal transfer schedule. Long-Evans rats were initially trained to respond to an auditory stimulus. All subjects were then implanted in both S1bfs with chronic microwire arrays for recording neural activity and delivering ICMS. Next, this association was transferred to ICMS of one hemisphere's S1bf. S1bf responded to ICMS with a brief increase in firing rate followed by a longer reduction in activity. We observed that the duration of reduced activity elicited by ICMS increased as the subjects began to respond correctly more often than expected by chance, and the magnitude of the initial positive response increased as they consolidated this CS-CR. Subsequent ICMS of the opposite S1bf revealed that this CS-CR did not generalize across hemispheres. These results suggest that a mechanism involving a single hemisphere's S1bf tunes cortical responses in concert with changes in rodent behavior during somatosensory learning.behavior; learning; microstimulation; rat; somatosensory learning THE RODENT VIBRISSA SYSTEM has proved a promising model system for investigating how the brain adaptively integrates sensory and motor information to generate perception (Diamond et al. 2008;Kleinfeld et al. 1999;Petersen 2007). Previous work has shown how the stimulus-driven responses of the vibrissa system, from the trigeminal ganglion to the somatosensory barrel cortex (S1bf), depend on the behavioral state of the animal (Leiser and Moxon 2007; Wiest and Nicolelis 2003). Specifically, the responses of S1bf neurons to whisker stimulation have been shown to vary dependent on whether the animal is anesthetized or awake (Petersen et al. 2003) and attentive or inattentive (Ferezou et al. 2007). Moreover, the functional reorganization of S1bf induced by sensory deprivation (Allen et al. 2002;Fox 2002) or aversive conditioning (Bekisz et al. 2010; Urban-Cieko et al. 2010) has demonstrated that the barrel cortex is highly plastic. Whether and how these changes in S1bf activity relate to the learning process remains unclear.The di...

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Section: Discussionmentioning

confidence: 64%

Section: Microstimulationmentioning

confidence: 99%

Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus

Long

Carmena

2013

Journal of Neurophysiology

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“…Interactions with the local network and single-neuron dynamics are thus likely to play a key role in shaping these properties. Defined interaction based on local ongoing spiking activity may need to follow only a few rules and could become a useful approach toward improved stimulus-response predictability and efficacy in neuroprosthetic devices (Marzullo et al 2010;Venkatraman et al 2009) and for improved efficiency of deep-brain stimulation devices in neurological disorders (Li and Mogul 2007;Rosin et al 2011;Shah et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Quantitative examination of stimulus-response relations in cortical networks in vitro

Weihberger

Okujeni

Mikkonen

et al. 2013

Journal of Neurophysiology

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Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms, such as cognitive context, network state, or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We identified network-intrinsic principles that underlie the formation and modulation of spontaneous activity and stimulus-response relations with the use of state-dependent stimulation in generic neuronal networks in vitro. The duration of spontaneously recurring network-wide bursts of spikes was best predicted by the length of the preceding interval. Length, delay, and structure of responses to identical stimuli systematically depended on stimulus timing and distance to the stimulation site, which were described by a set of simple functions of spontaneous activity. Response length at proximal recording sites increased with the duration of prestimulus inactivity and was best described by a saturation function y(t) ϭ A(1 Ϫ e Ϫ␣t ). Concomitantly, the delays of polysynaptic late responses at distant sites followed an exponential decay y(t) ϭ Be Ϫ␤t ϩ C. In addition, the speed of propagation was determined by the overall state of the network at the moment of stimulation. Disinhibition increased the number of spikes/network burst and interburst interval length at unchanged gross firing rate, whereas the response modulation by the duration of prestimulus inactivity was preserved. Our data suggest a process of network depression during bursts and subsequent recovery that limit evoked responses following distinct rules. We discuss shortterm synaptic depression due to depletion of neurotransmitter vesicles as an underlying mechanism. The seemingly unreliable patterns of spontaneous activity and stimulus-response relations thus follow a predictable structure determined by the interdependencies of network structures and activity states.

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“…One possibility would be to switch the recording amplifiers out of the circuit when ICMS pulses are delivered to prevent the recording amplifiers from being saturated (Freeman, 1971;Jimbo et al, 2003;Venkatraman et al, 2009). This alone may reduce the effective duration of the artifact less than 1 ms and would allow stimulation and recording on the same channel.…”

Section: Can Any Sensory Cortical Region Be Used As a Target For Icms?mentioning

confidence: 99%

Brain-Machine-Brain Interface

O’Doherty¹

2015

Encyclopedia of Computational Neuroscience

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I first show that a direct intracortical signal can be used to instruct rhesus monkeys about the direction of a reach to make with a BMI. Rhesus monkeys placed an actuator over an instruction target and obtained, from the target's artificial texture, information about the correct reach path. Initially these somatosensory instructions took the form of vibrotactile stimulation of the hands. Next, ICMS of primary somatosensory cortex (S1) iv in one monkey and posterior parietal cortex (PPC) in another was substituted for this peripheral somatosensory signal. Finally, the monkeys made direct brain-controlled reaches using the activity of ensembles of primary motor cortex (M1) cells, conditional on the ICMS cues. The monkey receiving ICMS of S1 was able to achieve the same level of proficiency with ICMS as with the stimulus delivered to the skin of the hand. The monkey receiving ICMS of PPC was unable to perform the task above chance. This experiment indicates that ICMS of S1 can form the basis of an afferent prosthetic input to the brain for guiding brain-controlled prostheses.I next show that ICMS of S1 can provide feedback about the interactions of a virtualreality upper-limb avatar and virtual objects, enabling active touch. Rhesus monkeys initially controlled the avatar with the movements of their arms and used it to search through sets of up to three objects. Feedback in the form of temporal patterns of ICMS occurred whenever the avatar touched a virtual object. Monkeys learned to use this feedback to find the objects with particular artificial textures, as encoded by the ICMS patterns, and select those associated with reward while avoiding selecting the non-rewarded objects. Next, the control of the avatar was switched to direct brain-control and the monkeys continued to move the avatar with motor commands derived from the extracellular neuronal activity of M1 cells. The afferent and efferent modules of this BMBI were temporally interleaved, and as such did not interfere with each other, yet allowed effectively concurrent operation. Cortical motor neurons were measured while the monkey passively observed the movements of the avatar and were found to be modulated, a result that suggests that concurrent visual and artificial somatosensory feedback lead to the incorporation of the avatar into the monkey's internal brain representation. In summary, rhesus monkeys were augmented with a bidirectional neural interface that allowed them to make reaches to objects and discriminate them by their textures-all without making actual movements and without relying on somatic sensation from their real bodies. Both action and perception were mediated by the brain-machine-brain interface.I probed the sensitivity of the afferent leg of the interface to precise temporal patterns of ICMS. Moreover, I describe evidence that the BMBI controlled avatar was incorporated into the monkey's internal brain representation. These results suggest that future clinical neuroprostheses could implement realistic feedback about object-actuator in...

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A System for Neural Recording and Closed-Loop Intracortical Microstimulation in Awake Rodents

Cited by 86 publications

References 39 publications

Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus

Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus

Quantitative examination of stimulus-response relations in cortical networks in vitro

Brain-Machine-Brain Interface

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