Siu Kang

Bidirectional plasticity in fast-spiking GABA circuits by visual experience.

Experience-dependent plasticity in the brain requires balanced excitation–inhibition. How individual circuit elements contribute to plasticity outcome in complex neocortical networks remains unknown. Here we report an intracellular analysis of ocular dominance plasticity—the loss of acuity and cortical responsiveness for an eye deprived of vision in early life. Unlike the typical progressive loss of pyramidal-cell bias, direct recording from fast-spiking cells in vivo reveals a counterintuitive initial shift towards the occluded eye followed by a late preference for the open eye, consistent with a spike-timing-dependent plasticity rule for these inhibitory neurons. Intracellular pharmacology confirms a dynamic switch of GABA (γ-aminobutyric acid) impact to pyramidal cells following deprivation in juvenile mice only. Together these results suggest that the bidirectional recruitment of an initially binocular GABA circuit may contribute to experience-dependent plasticity in the developing visual cortex.

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model.

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP–DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Hippocampal Polysynaptic Computation

Neural circuitry is a self-organizing arithmetic device that converts input to output and thereby remodels its computational algorithm to produce more desired output; however, experimental evidence regarding the mechanism by which information is modified and stored while propagating across polysynaptic networks is sparse. We used functional multineuron calcium imaging to monitor the spike outputs from thousands of CA1 neurons in response to the stimulation of two independent sites of the dentate gyrus in rat hippocampal networks ex vivo. Only pyramidal cells were analyzed based on post hoc immunostaining. Some CA1 pyramidal cells were observed to fire action potentials only when both sites were simultaneously stimulated (AND-like neurons), whereas other neurons fired in response to either site of stimulation but not to concurrent stimulation (XOR-like neurons). Both types of neurons were interlaced in the same network and altered their logical operation depending on the timing of paired stimulation. Repetitive paired stimulation for brief periods induced a persistent reorganization of AND and XOR operators, suggesting a flexibility in parallel distributed processing. We simulated these network functions in silico and found that synaptic modification of the CA3 recurrent excitation is pivotal to the shaping of logic plasticity. This work provides new insights into how microscopic synaptic properties are associated with the mesoscopic dynamics of complex microcircuits.

Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI) fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

Multiscale analysis of long-term recordings from unanesthetized rats unveils multiple time scales inherent in the neural dynamics

cyclase inhibitor 1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one (ODQ), protein kinase G inhibitor KT5823, cGMP analogue 8-bromo-cGMP (8-Br-cGMP), peroxynitrite donor 3-morpholinosydnonimine (SIN-1) or RyR blocker dantrolene for a period of 9–13 DIV. Treatment with L-NAME led to a significant decrease in NO2 level in the culture medium and intracellular cGMP level during the culture for 24 h. L-NAME, ODQ, and KT5823 markedly decreased 5′-bromo-2′-deoxyuridine (BrdU) incorporation into the NPCs the proliferative activity. Contrariwise, SIN-1 significantly increased BrdU incorporation. 8-Br-cGMP partially abolished the decrease in BrdU incorporation by L-NAME. However, no significant change was observed in lactate dehydrogenase released into the culture medium during treatment with any drugs. Treatment with dantrolene was effective in decreasing BrdU incorporation and NO2 level in the culture medium. RT-PCR analysis revealed that there mainly exist RyR3 of RyR1, RyR2, and RyR3 in the NPCs. These results suggest that RyR-mediated Ca2+ release from endoplasmic reticulum is essential for proliferative activity through activation of NO/cGMP pathway in the hippocampal NPCs of embryonic mice.

Seven-hour multiunit recordings from rats reveal very long-term correlation in the cortical activity

In the present study, we investigate how history of activity is retained in neurons embedded in the intact brain. For this purpose, we employ the so-called multiscale analysis for the fluctuations of interspike intervals (ISIs). An ability of neuronal networks, but not of isolated neurons, to retain information at different time scales greatly enriches their computational ability because now they can access the information across the full space-time domain, rather than spatially but at a single temporal scale. We analyze the intact brain of rats without anesthesia using a special chamber[1], namely the neural activity of the normally working brain. Recorded data with multiunit electrodes reveal evidence for the long-term in neuronal activity. We apply multiscale analysis because it has been proven to be powerful to uncover multiple time scales in hydrodynamic turbulence [2], human heartbeat interval fluctuations [3,4], stock price fluctuations [5], etc. In the multiscale analysis, we study concatenated ISIs instead of the original ISIs. We first determine a ‘scale’ that specifies how many consecutive ISI are concatenated (connected) and ask how large the fluctuations of the concatenated ISIs are. The upper panel of Figure 1 shows the fluctuations at scale of four (s=4) and the lower panel shows the corresponding histogram displayed in the semilog coordinate. Gaussian fluctuations should be shaped as a parabola in this coordinate. The histogram in Figure 2 implies that the fluctuations are highly non-Gaussian. As we increase the scale, more and more ISIs are concatenated. The central limiting theorem should ensure that the histogram become increasingly Gaussian if ISIs are statistically independent. However, Figure 3 shows that at scale s=32 or at even s=256, the histogram remains non-Gaussian. This implies very strong non-Gaussianity or strong long-term correlation of ISIs.