Karunesh Ganguly

GABA Itself Promotes the Developmental Switch of Neuronal GABAergic Responses from Excitation to Inhibition

GABA is the main inhibitory neurotransmitter in the adult brain. Early in development, however, GABAergic synaptic transmission is excitatory and can exert widespread trophic effects. During the postnatal period, GABAergic responses undergo a switch from being excitatory to inhibitory. Here, we show that the switch is delayed by chronic blockade of GABA(A) receptors, and accelerated by increased GABA(A) receptor activation. In contrast, blockade of glutamatergic transmission or action potentials has no effect. Furthermore, GABAergic activity modulated the mRNA levels of KCC2, a K(+)-Cl(-) cotransporter whose expression correlates with the switch. Finally, we report that GABA can alter the properties of depolarization-induced Ca(2+) influx. Thus, GABA acts as a self-limiting trophic factor during neural development.

Emergence of a Stable Cortical Map for Neuroprosthetic Control

In this article, the authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and resistant to interference.

Coincident Pre- and Postsynaptic Activity Modifies GABAergic Synapses by Postsynaptic Changes in Cl− Transporter Activity

Coincident pre- and postsynaptic activation is known to induce long-term modification of glutamatergic synapses. We report here that, in both hippocampal cultures and acute hippocampal slices, repetitive postsynaptic spiking within 20 ms before and after the activation of GABAergic synapses also led to a persistent change in synaptic strength. This synaptic modification required Ca2+ influx through postsynaptic L-type Ca2+ channels and was due to a local decrease in K+-Cl- cotransport activity, effectively reducing the strength of inhibition. Thus, GABAergic synapses can detect and be modified by coincident pre- and postsynaptic spiking, allowing the level of inhibition to be modulated in accordance to the temporal pattern of postsynaptic excitation.

Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies

Hebb proposed that neuronal cell assemblies are critical for effective perception, cognition, and action. However, evidence for brain mechanisms that coordinate multiple coactive assemblies remains lacking. Neuronal oscillations have been suggested as one possible mechanism for cell assembly coordination. Prior studies have shown that spike timing depends upon local field potential (LFP) phase proximal to the cell body, but few studies have examined the dependence of spiking on distal LFP phases in other brain areas far from the neuron or the influence of LFP–LFP phase coupling between distal areas on spiking. We investigated these interactions by recording LFPs and single-unit activity using multiple microelectrode arrays in several brain areas and then used a unique probabilistic multivariate phase distribution to model the dependence of spike timing on the full pattern of proximal LFP phases, distal LFP phases, and LFP–LFP phase coupling between electrodes. Here we show that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase. Neurons that prefer similar patterns of phase coupling exhibit similar changes in spike rates, whereas neurons with different preferences show divergent responses, providing a basic mechanism to bind different neurons together into coordinated cell assemblies. Surprisingly, phase-coupling–based rate correlations are independent of interneuron distance. Phase-coupling preferences correlate with behavior and neural function and remain stable over multiple days. These findings suggest that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.

Reversible large–scale modification of cortical networks during neuroprosthetic control

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Enhancement of presynaptic neuronal excitability by correlated presynaptic and postsynaptic spiking

Use-dependent modifications, such as long-term potentiation (LTP) of synaptic efficacy, are believed to be essential for information storage in the nervous system. Repetitive correlated spiking of presynaptic and postsynaptic neurons can induce LTP at excitatory glutamatergic synapses. In cultured hippocampal neurons, we show that repetitive correlated activity also results in a rapid and persistent enhancement of presynaptic excitability, decreasing the threshold for spiking and reducing the variability of interspike intervals. Furthermore, we found that correlated activity modified sodium channel gating in the presynaptic neuron. This modification of presynaptic excitability required a temporal order between presynaptic and postsynaptic spiking and activation of postsynaptic NMDA receptors. Presynaptic inhibition of protein kinase C abolished the change in excitability without affecting LTP. Such rapid activity-dependent changes in the efficacy of presynaptic spiking may be involved in the processing and storage of information within the nervous system.

Cortical Representation of Ipsilateral Arm Movements in Monkey and Man

A fundamental organizational principle of the primate motor system is cortical control of contralateral limb movements. Motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. Interestingly, this representation was more correlated with joint angles than hand position. Using bilateral electromyography recordings, we excluded the possibility that postural or mirror movements could exclusively account for these findings. In addition, linear methods could decode limb position from cortical field potentials in both monkeys. We also found that M1 spiking activity could control a biomimetic brain–machine interface reflecting ipsilateral kinematics. Finally, we recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain–machine interface.

Activity-Dependent Neural Plasticity from Bench to Bedside

Much progress has been made in understanding how behavioral experience and neural activity can modify the structure and function of neural circuits during development and in the adult brain. Studies of physiological and molecular mechanisms underlying activity-dependent plasticity in animal models have suggested potential therapeutic approaches for a wide range of brain disorders in humans. Physiological and electrical stimulations as well as plasticity-modifying molecular agents may facilitate functional recovery by selectively enhancing existing neural circuits or promoting the formation of new functional circuits. Here, we review the advances in basic studies of neural plasticity mechanisms in developing and adult nervous systems and current clinical treatments that harness neural plasticity, and we offer perspectives on future development of plasticity-based therapy.

Learning in Closed-Loop Brain–Machine Interfaces: Modeling and Experimental Validation

Closed-loop operation of a brain-machine interface (BMI) relies on the subjects ability to learn an inverse transformation of the plant to be controlled. In this paper, we propose a model of the learning process that undergoes closed-loop BMI operation. We first explore the properties of the model and show that it is able to learn an inverse model of the controlled plant. Then, we compare the model predictions to actual experimental neural and behavioral data from nonhuman primates operating a BMI, which demonstrate high accordance of the model with the experimental data. Applying tools from control theory to this learning model will help in the design of a new generation of neural information decoders which will maximize learning speed for BMI users.

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation

Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.