@article {3331, title = {A computational perspective of the role of Thalamus in cognition}, journal = {arxiv}, year = {2018}, abstract = {
Thalamus has traditionally been considered as only a relay source of cortical inputs, with hierarchically organized cortical circuits serially transforming thalamic signals to cognitively-relevant representations. Given the absence of local excitatory connections within the thalamus, the notion of thalamic {\textquoteleft}relay{\textquoteright} seemed like a reasonable description over the last several decades. Recent advances in experimental approaches and theory provide a broader perspective on the role of the thalamus in cognitively-relevant cortical computations, and suggest that only a subset of thalamic circuit motifs fit the relay description. Here, we discuss this perspective and highlight the potential role for the thalamus in dynamic selection of cortical representations through a combination of intrinsic thalamic computations and output signals that change cortical network functional parameters. We suggest that through the contextual modulation of cortical computation, thalamus and cortex jointly optimize the information/cost tradeoff in an emergent fashion. We emphasize that coordinated experimental and theoretical efforts will provide a path to understanding the role of the thalamus in cognition, along with an understanding to augment cognitive capacity in health and disease.
}, keywords = {Artificial Intelligence, Cognitive Computing, Multi-objective Optimization, Recurrent Neural Network, Reservoir Computing, Thalamo-cortical system}, url = {https://arxiv.org/abs/1803.00997}, author = {Nima Dehghani and Ralf Wimmer} } @article {2806, title = {Design of the Artificial: lessons from the biological roots of general intelligence}, year = {2017}, abstract = {
Our desire and fascination with intelligent machines dates back to the antiquity{\textquoteright}s mythical automaton Talos, Aristotle{\textquoteright}s mode of mechanical thought (syllogism) and Heron of Alexandria{\textquoteright}s mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.
}, url = {https://arxiv.org/pdf/1703.02245}, author = {Nima Dehghani} } @article {2810, title = {Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex}, journal = {Nature Scientific Reports}, year = {2017}, abstract = {

The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterized. We investigated this contribution in multi-electrode array recordings from the cerebral cortex of human and monkey by calculating the spike-triggered LFP average (st-LFP). The resulting st-LFPs were dominated by broad spatio-temporal components due to on-going activity, synaptic inputs and recurrent connectivity. To observe the local field of a single spike we applied spatial filtering. The filtered st-LFPs were limited to an area of 800 μm from the neuron, and propagated at axonal speed, which is consistent with their unitary nature. In addition, we discriminated between putative inhibitory and excitatory neurons and found that the former dominated this unitary LFP contribution, consistently with previous findings in hippocampal slices. Thus, in human and monkey cortex, the LFP may primarily reflect inhibitory neuron activity.

}, doi = {10.1038/srep40211}, url = {http://www.nature.com/articles/srep40211}, author = {Bartosz Telenczuk and Nima Dehghani and Michel Le Van Quyen and Sydney Cash and Eric Halgren and Nicholas Hatsopoulos and Alain Destexhe} } @article {2804, title = {Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective}, year = {2017}, abstract = {

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.

}, doi = {10.1101/097618}, url = {http://biorxiv.org/content/early/2017/01/01/097618.full.pdf+html}, author = {Nima Dehghani} } @article {2809, title = {Dynamic balance of excitation and inhibition in human and monkey neocortex}, journal = {Nature Scientific Reports}, year = {2016}, abstract = {

Balance of excitation and inhibition is a fundamental feature of\ in vivo\ network activity and is important for its computations. However, its presence in the neocortex of higher mammals is not well established. We investigated the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys. We found that in all states of the wake-sleep cycle, excitatory and inhibitory ensembles are well balanced, and co-fluctuate with slight instantaneous deviations from perfect balance, mostly in slow-wave sleep. Remarkably, these correlated fluctuations are seen for many different temporal scales. The similarity of these computational features with a network model of self-generated balanced states suggests that such balanced activity is essentially generated by recurrent activity in the local network and is not due to external inputs. Finally, we find that this balance breaks down during seizures, where the temporal correlation of excitatory and inhibitory populations is disrupted. These results show that balanced activity is a feature of normal brain activity, and break down of the balance could be an important factor to define pathological states.

}, doi = {10.1038/srep23176}, url = {http://www.nature.com/articles/srep23176}, author = {Nima Dehghani} } @article {2811, title = {High-frequency oscillations in human and monkey neocortex during the wake{\textendash}sleep cycle}, journal = {Proceedings of the National Academy of Sciences}, year = {2016}, doi = {10.1073/pnas.1523583113}, url = {http://www.pnas.org/content/113/33/9363}, author = {Michel Le Van Quyen and Lyle Muller and Bartosz Telenczuk and Eric Halgren and Sydney Cash and Nicholas Hatsopoulos and Nima Dehghani and Alain Destexhe} } @article {2813, title = {Avalanche analysis from multielectrode ensemble recordings in cat, monkey, and human cerebral cortex during wakefulness and sleep}, journal = {Frontiers in Physiology}, year = {2012}, abstract = {

Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different\ in vivo\ preparations during wakefulness, slow-wave sleep, and REM sleep, using high density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and premotor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double logarithmic representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using the more reliable cumulative distribution function (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey, and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.

}, doi = {10.3389/fphys.2012.00302}, url = {http://journal.frontiersin.org/article/10.3389/fphys.2012.00302}, author = {Nima Dehghani and Nicholas Hatsopoulos and Zach Haga and Rebecca Parker and Bradley Greger and Eric Halgren and Sydney Cash and Alain Destexhe} } @article {2812, title = {Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep}, journal = {Proceedings of the National Academy of Sciences}, year = {2012}, abstract = {

Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.

}, doi = {10.1073/pnas.1109895109}, url = {http://www.pnas.org/content/109/5/1731}, author = {Adrien Peyrache and Nima Dehghani and Emad Eskandar and Joseph Madsen and WS Anderson and Jacob Donoghue and Leigh Hochberg and Eric Halgren and Sydney Cash and Alain Destexhe} }