""" Cross-Species Neural Recording Protocols Standardized protocols for recording neural data across different species """ import math import json import datetime import hashlib from typing import Dict, List, Optional, Any, Union from dataclasses import dataclass import itertools @dataclass class SpeciesParameters: """Species-specific neural recording parameters""" name: str brain_volume: float # cm³ neuron_count: int # approximate typical_firing_rate: float # Hz eeg_characteristics: Dict[str, float] ethical_constraints: List[str] anesthesia_requirements: Dict[str, str] class CrossSpeciesProtocols: """Cross-species neural recording protocols""" def __init__(self): self.species_database = self._initialize_species_database() self.protocol_templates = self._initialize_protocol_templates() self.safety_guidelines = self._initialize_safety_guidelines() def _initialize_species_database(self) -> Dict[str, SpeciesParameters]: """Initialize species-specific parameters""" return { 'human': SpeciesParameters( name='Homo sapiens', brain_volume=1350.0, neuron_count=86000000000, typical_firing_rate=5.0, eeg_characteristics={ 'alpha_band': (8, 13), 'beta_band': (13, 30), 'gamma_band': (30, 100), 'typical_amplitude': 50.0 # μV }, ethical_constraints=['informed_consent', 'IRB_approval', 'minimal_risk'], anesthesia_requirements={'surgery': 'general', 'recording': 'none'} ), 'mouse': SpeciesParameters( name='Mus musculus', brain_volume=0.4, neuron_count=71000000, typical_firing_rate=8.0, eeg_characteristics={ 'alpha_band': (8, 13), 'beta_band': (13, 30), 'gamma_band': (30, 100), 'typical_amplitude': 100.0 # μV }, ethical_constraints=['IACUC_approval', 'minimal_pain', 'humane_endpoints'], anesthesia_requirements={'surgery': 'isoflurane', 'recording': 'urethane'} ), 'rat': SpeciesParameters( name='Rattus norvegicus', brain_volume=2.0, neuron_count=200000000, typical_firing_rate=6.0, eeg_characteristics={ 'alpha_band': (7, 12), 'beta_band': (12, 30), 'gamma_band': (30, 80), 'typical_amplitude': 150.0 # μV }, ethical_constraints=['IACUC_approval', 'minimal_pain', 'environmental_enrichment'], anesthesia_requirements={'surgery': 'isoflurane', 'recording': 'urethane'} ), 'monkey': SpeciesParameters( name='Macaca mulatta', brain_volume=90.0, neuron_count=6000000000, typical_firing_rate=7.0, eeg_characteristics={ 'alpha_band': (8, 12), 'beta_band': (13, 30), 'gamma_band': (30, 90), 'typical_amplitude': 75.0 # μV }, ethical_constraints=['IACUC_approval', 'psychological_wellbeing', 'social_housing'], anesthesia_requirements={'surgery': 'isoflurane', 'recording': 'minimal_sedation'} ), 'fly': SpeciesParameters( name='Drosophila melanogaster', brain_volume=0.0002, neuron_count=100000, typical_firing_rate=20.0, eeg_characteristics={ 'alpha_band': (10, 20), 'beta_band': (20, 40), 'gamma_band': (40, 100), 'typical_amplitude': 5.0 # μV }, ethical_constraints=['minimal_suffering', 'proper_housing'], anesthesia_requirements={'surgery': 'CO2', 'recording': 'none'} ) } def _initialize_protocol_templates(self) -> Dict[str, Dict]: """Initialize protocol templates""" return { 'electrophysiology': { 'description': 'Single-unit and multi-unit electrophysiological recordings', 'required_equipment': ['microelectrodes', 'amplifier', 'digitizer', 'recording_system'], 'typical_duration': '1-4 hours', 'sampling_rates': { 'human': 20000, 'monkey': 30000, 'rat': 30000, 'mouse': 30000, 'fly': 20000 }, 'electrode_types': { 'human': ['ECoG', 'depth_electrodes', 'surface_electrodes'], 'monkey': ['Utah_array', 'Michigan_probe', 'tetrodes'], 'rat': ['tetrodes', 'silicon_probes', 'microwires'], 'mouse': ['tetrodes', 'silicon_probes', 'patch_clamp'], 'fly': ['tungsten_electrodes', 'glass_pipettes'] } }, 'eeg': { 'description': 'Electroencephalography recordings', 'required_equipment': ['EEG_cap', 'amplifier', 'digitizer'], 'typical_duration': '30 minutes - 2 hours', 'sampling_rates': { 'human': 1000, 'monkey': 1000, 'rat': 500, 'mouse': 500, 'fly': 2000 }, 'electrode_layouts': { 'human': ['10-20_system', '10-10_system', 'high_density'], 'monkey': ['custom_cylinder', 'surface_grid'], 'rat': ['bipolar', 'monopolar'], 'mouse': ['bipolar', 'monopolar'], 'fly': ['differential', 'single_ended'] } }, 'fmri': { 'description': 'Functional magnetic resonance imaging', 'required_equipment': ['MRI_scanner', 'rf_coils', 'stimulus_delivery'], 'typical_duration': '45-90 minutes', 'sampling_rates': { 'human': 1, 'monkey': 1, 'rat': 1, 'mouse': 1, 'fly': 'not_applicable' }, 'field_strengths': { 'human': ['3T', '7T'], 'monkey': ['3T', '4.7T', '7T'], 'rat': ['4.7T', '7T', '9.4T'], 'mouse': ['7T', '9.4T', '11.7T'], 'fly': ['not_applicable'] } }, 'two_photon': { 'description': 'Two-photon calcium imaging', 'required_equipment': ['two_photon_microscope', 'fluorescent_indicators', 'cranial_window'], 'typical_duration': '30-90 minutes', 'sampling_rates': { 'human': 'not_applicable', 'monkey': '30 Hz', 'rat': '30 Hz', 'mouse': '30 Hz', 'fly': '10 Hz' }, 'indicators': { 'human': 'not_applicable', 'monkey': ['GCaMP6', 'RCaMP'], 'rat': ['GCaMP6', 'jRGECO'], 'mouse': ['GCaMP6', 'GCaMP7', 'jRGECO'], 'fly': ['GCaMP6', 'GCaMP7'] } } } def _initialize_safety_guidelines(self) -> Dict[str, List[str]]: """Initialize safety guidelines""" return { 'electrical_safety': [ 'ground_fault_protection', 'current_limiting', 'isolation_amplifiers', 'regular_equipment_inspection' ], 'biological_safety': [ 'sterile_technique', 'antibiotic_prophylaxis', 'infection_monitoring', 'proper_wound_care' ], 'physiological_monitoring': [ 'heart_rate_monitoring', 'respiration_monitoring', 'temperature_monitoring', 'blood_pressure_monitoring' ], 'emergency_procedures': [ 'anesthesia_overdose_protocol', 'seizure_management', 'cardiovascular_emergency', 'equipment_failure_protocol' ] } def calculate_optimal_sampling_rate(self, species: str, modality: str) -> int: """Calculate optimal sampling rate for species-modality combination""" if species not in self.species_database: raise ValueError(f"Unknown species: {species}") if modality not in self.protocol_templates: raise ValueError(f"Unknown modality: {modality}") base_rate = self.protocol_templates[modality]['sampling_rates'].get(species, 1000) species_params = self.species_database[species] # Adjust based on species-specific characteristics if modality == 'electrophysiology': # Higher for smaller brains (faster dynamics) size_factor = math.log10(species_params.neuron_count / 1e6) rate_adjustment = math.exp(-size_factor / 2) return int(base_rate * max(0.5, min(2.0, rate_adjustment))) elif modality == 'eeg': # Adjust based on typical firing rate rate_adjustment = species_params.typical_firing_rate / 5.0 return int(base_rate * max(0.5, min(1.5, rate_adjustment))) return base_rate def generate_recording_protocol(self, species: str, modality: str, experiment_type: str = 'basic') -> Dict[str, Any]: """Generate complete recording protocol for species-modality combination""" if species not in self.species_database: raise ValueError(f"Unknown species: {species}") if modality not in self.protocol_templates: raise ValueError(f"Unknown modality: {modality}") species_params = self.species_database[species] protocol_template = self.protocol_templates[modality] # Generate protocol protocol = { 'species': species, 'modality': modality, 'experiment_type': experiment_type, 'timestamp': datetime.datetime.now().isoformat(), 'species_parameters': { 'brain_volume': species_params.brain_volume, 'neuron_count': species_params.neuron_count, 'typical_firing_rate': species_params.typical_firing_rate }, 'recording_parameters': { 'sampling_rate': self.calculate_optimal_sampling_rate(species, modality), 'duration': protocol_template['typical_duration'], 'required_equipment': protocol_template['required_equipment'] }, 'ethical_requirements': species_params.ethical_constraints, 'anesthesia_requirements': species_params.anesthesia_requirements, 'safety_guidelines': self.safety_guidelines } # Add modality-specific parameters if modality == 'eeg': protocol['recording_parameters']['eeg_bands'] = species_params.eeg_characteristics protocol['recording_parameters']['typical_amplitude'] = species_params.eeg_characteristics['typical_amplitude'] protocol['recording_parameters']['electrode_layout'] = protocol_template['electrode_layouts'][species] elif modality == 'electrophysiology': protocol['recording_parameters']['electrode_types'] = protocol_template['electrode_types'][species] elif modality == 'fmri': if protocol_template['field_strengths'][species] != ['not_applicable']: protocol['recording_parameters']['field_strength'] = protocol_template['field_strengths'][species] else: protocol['recording_parameters']['field_strength'] = 'not_applicable' # Add experiment-specific modifications if experiment_type == 'high_density': if modality == 'eeg': protocol['recording_parameters']['electrode_count'] = 256 if species == 'human' else 64 elif modality == 'electrophysiology': protocol['recording_parameters']['channel_count'] = 128 if species in ['human', 'monkey'] else 32 elif experiment_type == 'long_term': protocol['recording_parameters']['duration'] = '24-72 hours' protocol['recording_parameters']['implantable'] = True # Generate protocol hash for tracking protocol_string = json.dumps(protocol, sort_keys=True) protocol['protocol_hash'] = hashlib.sha256(protocol_string.encode()).hexdigest()[:16] return protocol def cross_species_comparison(self, modalities: List[str]) -> Dict[str, Any]: """Generate cross-species comparison for specified modalities""" comparison = { 'timestamp': datetime.datetime.now().isoformat(), 'modalities': modalities, 'species_comparison': {} } for species in self.species_database.keys(): species_comparison = { 'brain_volume': self.species_database[species].brain_volume, 'neuron_count': self.species_database[species].neuron_count, 'optimal_parameters': {} } for modality in modalities: if modality in self.protocol_templates: try: sampling_rate = self.calculate_optimal_sampling_rate(species, modality) species_comparison['optimal_parameters'][modality] = { 'sampling_rate': sampling_rate, 'duration': self.protocol_templates[modality]['typical_duration'] } except: species_comparison['optimal_parameters'][modality] = 'not_applicable' comparison['species_comparison'][species] = species_comparison return comparison def validate_protocol(self, protocol: Dict[str, Any]) -> Dict[str, Any]: """Validate a generated protocol""" validation_results = { 'timestamp': datetime.datetime.now().isoformat(), 'protocol_hash': protocol.get('protocol_hash', 'unknown'), 'validation_passed': True, 'warnings': [], 'errors': [] } # Check required fields required_fields = ['species', 'modality', 'recording_parameters', 'ethical_requirements'] for field in required_fields: if field not in protocol: validation_results['errors'].append(f"Missing required field: {field}") validation_results['validation_passed'] = False # Check species validity if 'species' in protocol and protocol['species'] not in self.species_database: validation_results['errors'].append(f"Unknown species: {protocol['species']}") validation_results['validation_passed'] = False # Check modality validity if 'modality' in protocol and protocol['modality'] not in self.protocol_templates: validation_results['errors'].append(f"Unknown modality: {protocol['modality']}") validation_results['validation_passed'] = False # Safety checks if 'recording_parameters' in protocol: rec_params = protocol['recording_parameters'] if 'sampling_rate' in rec_params: if rec_params['sampling_rate'] > 50000: validation_results['warnings'].append("Very high sampling rate may cause data overload") elif rec_params['sampling_rate'] < 100: validation_results['warnings'].append("Low sampling rate may miss fast neural dynamics") return validation_results def test_cross_species_protocols(): """Test the cross-species protocols""" print("Testing Cross-Species Neural Recording Protocols...") protocols = CrossSpeciesProtocols() # Test protocol generation print("\n1. Testing protocol generation...") for species in ['human', 'mouse', 'monkey']: for modality in ['eeg', 'electrophysiology']: try: protocol = protocols.generate_recording_protocol(species, modality) print(f"{species}-{modality}: {protocol['recording_parameters']['sampling_rate']} Hz") except Exception as e: print(f"{species}-{modality}: Error - {e}") # Test optimal sampling rate calculation print("\n2. Testing optimal sampling rates...") for species in protocols.species_database.keys(): eeg_rate = protocols.calculate_optimal_sampling_rate(species, 'eeg') electrophys_rate = protocols.calculate_optimal_sampling_rate(species, 'electrophysiology') print(f"{species}: EEG={eeg_rate} Hz, Electrophysiology={electrophys_rate} Hz") # Test cross-species comparison print("\n3. Testing cross-species comparison...") comparison = protocols.cross_species_comparison(['eeg', 'electrophysiology']) for species, data in comparison['species_comparison'].items(): print(f"{species}: {data['brain_volume']:.4f} cm³, {data['neuron_count']:,} neurons") # Test protocol validation print("\n4. Testing protocol validation...") valid_protocol = protocols.generate_recording_protocol('human', 'eeg') validation = protocols.validate_protocol(valid_protocol) print(f"Valid protocol validation: {'PASSED' if validation['validation_passed'] else 'FAILED'}") # Test invalid protocol invalid_protocol = {'species': 'alien', 'modality': 'telepathy'} invalid_validation = protocols.validate_protocol(invalid_protocol) print(f"Invalid protocol validation: {'PASSED' if invalid_validation['validation_passed'] else 'FAILED'}") print(f"Errors: {invalid_validation['errors']}") return True if __name__ == "__main__": test_cross_species_protocols()