Theoretical Neuroscience¶
Dayan, P., & Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Micro Macro Linkage
- In neuroscience, spike statistics aggregate to population codes and field potentials while brain-state variables modulate individual neuron responsiveness, underlying large-scale phenomena like synchrony and seizures.
This sourceDevelops neural population coding — single-neuron spike statistics aggregating to population codes — and how network/brain-state variables modulate single-neuron responsiveness, the upward and downward edges of micro-macro coupling in the brain.
- In neuroscience, spike statistics aggregate to population codes and field potentials while brain-state variables modulate individual neuron responsiveness, underlying large-scale phenomena like synchrony and seizures.
- Poisson Process
- The identical diagnostic ladder governs a call center modeling arrivals as Poisson input to size staffing, where burstiness above Poisson signals correlated demand (a marketing blast) needing surge capacity, and a neuroscientist testing a spike train, where under-dispersion reveals neural refractoriness and bursting reveals network excitation — the same fit-rate, check-commitments, name-the-departure procedure in each.
This sourceModels cortical spike trains as (inhomogeneous) Poisson processes and reads refractoriness (under-dispersion) and bursting (over-dispersion) as diagnostic departures.
- The identical diagnostic ladder governs a call center modeling arrivals as Poisson input to size staffing, where burstiness above Poisson signals correlated demand (a marketing blast) needing surge capacity, and a neuroscientist testing a spike train, where under-dispersion reveals neural refractoriness and bursting reveals network excitation — the same fit-rate, check-commitments, name-the-departure procedure in each.
- Rate Coding
- Encoding a magnitude as a rate trades temporal resolution, set by the integration-window length, against signal precision, which improves as the inverse square root of the event count because the Poisson noise falls as one over the root of the number integrated.
This sourceStandard reference for rate coding: magnitude carried by spike-count over a window, with Poisson spike-count noise so precision improves as the inverse square root of the count.
- Encoding a magnitude as a rate trades temporal resolution, set by the integration-window length, against signal precision, which improves as the inverse square root of the event count because the Poisson noise falls as one over the root of the number integrated.
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