A whole-cell recording database of neuromodulatory action in the adult neocortex

Yan, Xuan; Calcini, Niccolò; Safavi, Payam; Ak, Asli; Kole, Koen; Zeldenrust, Fleur; Celikel, Tansu

doi:10.1101/2022.01.12.476007

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…A hallmark of parameter variations in nonlinear dynamical systems is that system properties do not always change smoothly, but can undergo critical qualitative transitions. This has been shown in biological neurons [3,10,11], but is also expected in neuromorphic hardware with nonlinear single-neuron dynamics. Here, we show that neuron-intrinsic parameter variation on nonlinear neuromorphic hardware has a significant effect on single-neuron computation and thereby on network dynamics.…”

Section: Introductionmentioning

confidence: 56%

Qualitative switches in single-neuron spike dynamics on neuromorphic hardware: implementation, impact on network synchronization and relevance for plasticity

Weerdmeester,

Niemeyer,

Pfeiffer

et al. 2024

Neuromorph. Comput. Eng.

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Most efforts on spike-based learning on neuromorphic hardware focus on synaptic plasticity and do not yet exploit the potential of altering the spike-generating dynamics themselves. Biological neurons show distinct mechanisms of spike generation, which affect single-neuron and network computations. Such a variety of spiking mechanisms can only be mimicked on chips with more advanced, nonlinear single-neuron dynamics than the commonly implemented leaky integrate-and-fire (LIF) neurons. Here, we demonstrate that neurons on the BrainScaleS-2 chip configured for exponential leaky integrate-and-fire (EIF) dynamics can be tuned to undergo a qualitative switch in spike generation via a modulation of the reset voltage. This switch is accompanied by altered synchronization properties of neurons in a network and thereby captures a main characteristic of the unfolding of the saddle-node loop (SNL) bifurcation - a qualitative transition that was recently demonstrated in biological neurons. Using this switch, cell-intrinsic properties alone provide a means to control whether small networks of all-to-all coupled neurons on the chip exhibit synchronized firing or splayed-out spiking patterns. We use an example from a central pattern generating circuit in the fruitfly to show that such dynamics can be induced and controlled on the chip. Our study thereby demonstrates the potential of neuromorphic chips with relatively complex and tunable single-neuron dynamics such as the BrainScaleS-2 chip, to generate computationally distinct single unit dynamics. We conclude with a discussion of the utility of versatile spike-generating mechanisms on neuromorphic chips.

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Section: Introductionmentioning

confidence: 56%

Qualitative switches in single-neuron spike dynamics on neuromorphic hardware: implementation, impact on network synchronization and relevance for plasticity

Weerdmeester,

Niemeyer,

Pfeiffer

et al. 2024

Neuromorph. Comput. Eng.

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“…This had of 2 reasons: the first was an experimental setup constraint: the current had to be generated offline and loaded onto the setup, which would have made the experiments take a lot longer to perform if we did not use the same current each time. The second was that the experiments were in many neurons also repeated with different neuromodulators [ 14 ]. For proper comparisons between aCSF and neuromodulator trials (ie omitted or added spikes for the same input), the input current needed to be frozen.…”

Section: Methodsmentioning

confidence: 99%

“…We start by measuring the intrinsic information encoding properties of putative excitatory (regular-spiking) and inhibitory (fast-spiking) neurons in L2/3 of the mouse barrel cortex. We measure the effects of several intrinsic neural characteristics on the information transfer from input current to output spike train, using a combination of ex-vivo experiments [ 13 , 14 ] and computational modelling. We aim to unravel how both the threshold behaviour and the I-V curve shape of excitatory and inhibitory neurons affect information transfer, using a recently developed method to estimate the mutual information between input and output in an ex-vivo setup [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

The tuning of tuning: How adaptation influences single cell information transfer

Zeldenrust,

Calcini,

Yan

et al. 2024

PLoS Comput Biol

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Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. To effectively transfer information about the stimulus to the next processing level, a neuron needs to be able to adapt its working range to the properties of the stimulus. Here, we focus on the intrinsic neural properties that influence information transfer in cortical neurons and how tightly their properties need to be tuned to the stimulus statistics for them to be effective. We start by measuring the intrinsic information encoding properties of putative excitatory and inhibitory neurons in L2/3 of the mouse barrel cortex. Excitatory neurons show high thresholds and strong adaptation, making them fire sparsely and resulting in a strong compression of information, whereas inhibitory neurons that favour fast spiking transfer more information. Next, we turn to computational modelling and ask how two properties influence information transfer: 1) spike-frequency adaptation and 2) the shape of the IV-curve. We find that a subthreshold (but not threshold) adaptation, the ‘h-current’, and a properly tuned leak conductance can increase the information transfer of a neuron, whereas threshold adaptation can increase its working range. Finally, we verify the effect of the IV-curve slope in our experimental recordings and show that excitatory neurons form a more heterogeneous population than inhibitory neurons. These relationships between intrinsic neural features and neural coding that had not been quantified before will aid computational, theoretical and systems neuroscientists in understanding how neuronal populations can alter their coding properties, such as through the impact of neuromodulators. Why the variability of intrinsic properties of excitatory neurons is larger than that of inhibitory ones is an exciting question, for which future research is needed.

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The tuning of tuning: how adaptation influences single cell information transfer

Zeldenrust

Calcini

Yan

et al. 2020

Preprint

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Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

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A whole-cell recording database of neuromodulatory action in the adult neocortex

Cited by 4 publications

References 42 publications

Qualitative switches in single-neuron spike dynamics on neuromorphic hardware: implementation, impact on network synchronization and relevance for plasticity

Qualitative switches in single-neuron spike dynamics on neuromorphic hardware: implementation, impact on network synchronization and relevance for plasticity

The tuning of tuning: How adaptation influences single cell information transfer

The tuning of tuning: how adaptation influences single cell information transfer

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