| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on electrical and chemical signaling in the nervous system and its control of organismal function. Includes membrane excitability, synaptic transmission, neurotransmitter dynamics, sensory encoding, motor control, and network integration. Excludes whole-organism behavioral ecology, psychiatric-level cognition, and molecular genetics except when directly shaping neuronal signaling. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from subcellular ion-channel domains (nanometers, microseconds) to whole-neuron and small-network scales (micrometers–centimeters, milliseconds–seconds). |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Neurons, glial cells, ion channels, receptors, synapses, neurotransmitters, action potentials, dendrites, axons, myelin, interneuronal networks, neuromodulators, and extracellular ionic environments. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Membrane potential, conductance, ion permeability, firing threshold, neurotransmitter concentration, synaptic strength, refractory periods, excitability, and transmission probability. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Excitatory vs inhibitory neurons, chemical vs electrical synapses, ion-channel classes, neurotransmitter systems, network motifs, firing types, and sensory–motor pathways. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Voltage, ionic gradients, conductance states, firing rate, synaptic weight, neurotransmitter release probability, intracellular Ca²⁺, and network activity levels. |
| | Parameterization | How variables encode and represent the system’s state. | State encoded through voltage traces, spike trains, synaptic-strength maps, conductance models, neurotransmitter-release kinetics, and network-activity spectra. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating neurons as point-neurons, modeling channels as two-state systems, reducing networks to simplified motifs, ignoring dendritic computation, linearizing nonlinear firing dynamics, or assuming homogeneous extracellular ion levels. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Idealizations fail during complex dendritic integration, pathological ion imbalance, nonlinear bursting, neuromodulation-driven state shifts, fast network oscillations, or high-dimensional circuit behavior. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes deterministic ion-channel kinetics, continuous membrane dynamics, stable electrochemical gradients, interpretable synaptic rules, and network behavior emerging from structured connectivity. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes neurons maintain consistent excitability rules, synaptic transmission reflects underlying molecular machinery, and extracellular conditions remain physiologically meaningful for signal propagation. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Firing dynamics, synaptic behavior, and ionic mechanisms must be mutually consistent across scales and cannot contradict known biophysical constraints. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (neurons, channels, synapses), variables (voltage, Ca²⁺, conductance), and assumptions (continuity, deterministic kinetics) must fit into a coherent signaling framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Action potentials, synaptic potentials, ionic currents, neurotransmitter release, intracellular Ca²⁺ transients, oscillatory rhythms, firing-rate changes, sensory receptor potentials, and network synchronization. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Minimum resolvable voltage changes (µV), smallest detectable ionic currents (pA), lower limits of Ca²⁺ indicator sensitivity, temporal limits of electrophysiological equipment (sub-ms), and spatial limits of neuronal imaging (nm–µm). |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | mV (voltage), pA/nA (current), Hz (firing rate), µM (neurotransmitter), fluorescence intensity units, ms (timing), and µm (morphological scale). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Patch-clamp amplifiers, multielectrode arrays, calcium imaging systems, voltage-sensitive dyes, two-photon microscopes, extracellular electrodes, EEG/MEG setups, optogenetic stimulation systems, and neurotransmitter sensors. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Definitions for “spike,” “synaptic event,” “EPSC/IPSC,” “firing rate,” “burst,” “oscillation frequency,” and “neurotransmitter release event,” tied to specific measurement thresholds and criteria. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standard protocols including whole-cell recordings, voltage-clamp and current-clamp modes, field potential recordings, Ca²⁺-indicator loading, optogenetic stimulation routines, and synaptic-stimulation paradigms. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Continuous high-frequency electrical sampling, time-lapse imaging, spike sorting, synaptic-event extraction, evoked-response paradigms, and repeated recordings across multiple conditions. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Selecting neurons, brain regions, compartments (soma/dendrites/axon), synapses, network states, and stimulus conditions in ways that maintain representative electrophysiological and signaling datasets. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Voltage traces, current traces, spike trains, raster plots, firing-rate time series, Ca²⁺ fluorescence time courses, synaptic-event logs, network spectrograms, and extracellular field recordings. |
| | Resolution | The granularity or precision with which data is captured. | Temporal resolution down to microseconds for electrophysiology; spatial resolution at nm–µm for imaging; amplitude resolution limited by amplifier noise and optical-signal dynamic range. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibrating amplifiers, verifying series resistance, calibrating Ca²⁺ indicator fluorescence, adjusting baseline drift, validating electrode impedance, optogenetic intensity mapping, and stimulus-timing verification. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Noise sources: thermal and electronic noise, series-resistance error, photobleaching, motion artifacts, spike-sorting ambiguity, synaptic-failure variability, and cell-to-cell physiological variation. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Core principles such as the voltage–current relationship, Hodgkin–Huxley channel kinetics, refractory-period limits, synaptic plasticity rules (e.g., Hebbian/LTP/LTD), rate coding, temporal coding, and oscillation–synchrony coupling. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable electrophysiological constants: reversal potentials (given ion gradients), fixed refractory periods, conserved firing-pattern motifs, stereotyped spike shapes, and stable neurotransmitter-specific receptor kinetics. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include ion-channel gating, synaptic vesicle release, receptor activation, dendritic integration, action potential propagation, neuromodulatory tuning, and recurrent network feedback loops. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Sequences such as stimulus → receptor transduction → graded potential → action potential → synaptic release → postsynaptic integration → network output. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Membrane excitability, threshold, conductance, synaptic weight, temporal summation, spatial summation, oscillations, network motifs, neuromodulation, encoding, and excitatory/inhibitory balance. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Neuron types (excitatory vs inhibitory), synapse types (chemical vs electrical), network architectures (feedforward, recurrent), firing classes (regular-spiking, bursting, fast-spiking), and neurotransmitter systems. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Hodgkin–Huxley equations, Nernst/Goldman equations, synaptic current formulas, cable theory equations, spike-timing–dependent plasticity (STDP) kernels, and dynamical-systems equations for oscillatory networks. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Compartmental neuron models, integrate-and-fire models, conductance-based channel models, synaptic plasticity models, network dynamical models, and biophysical simulations of spike propagation. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Point-neuron models, two-state channel models, linearized membrane approximations, simplified dendritic trees, uniform-synapse assumptions, and reduced firing-rate models. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under moderate input, typical ion concentrations, stable extracellular space, and low-complexity circuits; break down in nonlinear bursting regimes, neuromodulator-driven state shifts, pathological ionic imbalance, or dense recurrent networks. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integrative frameworks linking ion-channel dynamics, membrane biophysics, synaptic integration, and network computation, such as excitation–inhibition balance theory, dynamical-systems approaches, and neuro-computational coding theories. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Strong ties to biophysics, computational neuroscience, systems biology, electrophysiology, cognitive neuroscience, and biomedical engineering through shared principles of signaling, dynamics, and circuitry. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating ionic concentrations, membrane potentials, synaptic inputs, receptor activation, neuromodulator levels, or current injection to test causal effects on neuronal signaling and excitability. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Recording spontaneous neuronal activity, synaptic events, oscillatory rhythms, or natural sensory responses without applying controlled perturbations. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing predictions about channel kinetics, synaptic efficacy, plasticity mechanisms, firing thresholds, or network-state transitions through structured stimulation or pharmacological manipulation. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating electrophysiological experiments, imaging trials, synaptic-release measurements, or network-state recordings across multiple cells, slices, animals, or experimental runs. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Applying spike-train statistics, regression models, dynamical-systems fitting, spectral analysis, mixed-effects models, and Bayesian inference to interpret noisy neurophysiological data. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing conductance-based models, integrate-and-fire models, synaptic-plasticity frameworks, network dynamical models, and biophysical neuron models based on fit, predictive accuracy, and explanatory coherence. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying noise from electrode drift, thermal noise, synaptic variability, optical artifacts, imperfect spike sorting, preparation-induced stress, or instability of intracellular recordings. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Standardizing stimulus protocols, blinding during spike sorting or imaging quantification, calibrating electrodes and optical systems, controlling solution composition, and matching input patterns across replicates. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent assessment of electrophysiological traces, firing-property claims, synaptic-plasticity models, and computational frameworks via peer review, replication, and multi-lab comparison. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating neuron/synapse models, plasticity rules, network-theory assumptions, or excitability frameworks when new evidence contradicts canonical biophysical or computational interpretations. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of stimulus parameters, recording settings, filtering steps, analysis algorithms, electrode properties, pharmacological conditions, and modeling assumptions. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ensuring ethical use of animals and tissues, minimizing neuronal damage, honest reporting of results, avoiding data manipulation, and maintaining rigorous electrophysiological and imaging standards. |