ffsshhttiikk Neurobiology
What I Do
Neurobiology studies the nervous system from molecular to systems level. I cover neuronal structure and function, synaptic transmission, neural circuits, brain regions, sensory systems, motor control, learning and memory, and neuropharmacology. I help understand brain function and neurological processes.
When to Use Me
- Understanding neuronal electrophysiology
- Studying synaptic transmission and plasticity
- Analyzing neural circuit function
- Understanding sensory processing
- Studying learning and memory mechanisms
- Working with neurodegenerative diseases
- Developing neuropharmacology and drug targets
Core Concepts
- Neuronal Structure: Axons, dendrites, soma, myelin, synapses
- Action Potentials: Sodium-potassium pumps, voltage-gated channels
- Synaptic Transmission: Neurotransmitters, receptors, vesicle release
- Synaptic Plasticity: LTP, LTD, long-term potentiation
- Neural Circuits: Feedforward, feedback, oscillatory circuits
- Brain Regions: Cortex, hippocampus, basal ganglia, cerebellum
- Sensory Systems: Visual, auditory, somatosensory pathways
- Motor Control: Corticospinal tract, basal ganglia loops
- Neurotransmitters: Glutamate, GABA, dopamine, acetylcholine, serotonin
- Neuroplasticity: Structural and functional brain changes
Code Examples
import numpy as np
from typing import List, Dict, Tuple
class Electrophysiology:
def __init__(self, neuron_type: str):
self.neuron_type = neuron_type
def calculate_membrane_potential(self, na_out: float,
k_out: float,
cl_out: float,
na_in: float,
k_in: float,
cl_in: float) -> float:
R = 8.314
T = 310
F = 96485
E_na = (R * T / F) * np.log(na_out / na_in)
E_k = (R * T / F) * np.log(k_out / k_in)
E_cl = -(R * T / F) * np.log(cl_out / cl_in)
P_na = 0.01
P_k = 1.0
P_cl = 0.45
V_m = (P_na * E_na + P_k * E_k + P_cl * E_cl) / (P_na + P_k + P_cl)
return V_m
def nernst_potential(self, ion_out: float,
ion_in: float,
valence: int,
temperature: float = 310) -> float:
R = 8.314
F = 96485
return (R * temperature / (valence * F)) * np.log(ion_out / ion_in)
def ghk_equation(self, P_K: float, P_Na: float, P_Cl: float,
K_o: float, K_i: float,
Na_o: float, Na_i: float,
Cl_o: float, Cl_i: float) -> float:
R = 8.314
T = 310
F = 96485
term_K = P_K * K_o - P_K * K_i * np.exp(-F / (R * T) * 0.07)
term_Na = P_Na * Na_o - P_Na * Na_i * np.exp(-F / (R * T) * 0.07)
term_Cl = P_Cl * Cl_o * np.exp(-F / (R * T) * 0.07) - P_Cl * Cl_i
return (R * T / F) * np.log(
(term_K + term_Na) / (term_Cl + 1e-9)
)
def action_potential_threshold(self, v_rest: float,
density_na: float,
density_k: float) -> float:
return v_rest + 10 / (density_na / density_k)
def refractory_period(self, recovery_time: float,
temperature: float) -> float:
q10 = 2.5 # Q10 for ion channel recovery
return recovery_time / (q10 ** ((temperature - 37) / 10))
class SynapticTransmission:
def __init__(self, synapse_type: str):
self.synapse_type = synapse_type
def calculate_epsp(self, neurotransmitter: float,
receptor_count: float,
ec50: float,
hill_coefficient: float) -> float:
response = (neurotransmitter ** hill_coefficient) / \
(ec50 ** hill_coefficient + neurotransmitter ** hill_coefficient)
return response * receptor_count
def vesicle_release_probability(self, calcium_influx: float,
calcium_sensitivity: float) -> float:
return 1 / (1 + np.exp(-calcium_influx / calcium_sensitivity))
def synaptic_delay(self, distance: float,
conduction_velocity: float) -> float:
return distance / conduction_velocity
def short_term_plasticity(self, baseline_release: float,
depression_factor: float,
facilitation_factor: float,
pulse_number: int) -> float:
release_prob = baseline_release
for i in range(pulse_number):
release_prob *= depression_factor
release_prob += facilitation_factor * (1 - release_prob)
return release_prob
class NeuralCircuits:
def __init__(self, circuit_name: str):
self.circuit = circuit_name
def calculate_gain(self, input_amplitude: float,
output_amplitude: float) -> float:
return output_amplitude / input_amplitude
def lateral_inhibition(self, center_excitation: float,
surround_inhibition: float,
inhibition_strength: float) -> float:
return center_excitation - inhibition_strength * surround_inhibition
def receptive_field_center(self, rf_surround: float,
center_weight: float) -> float:
return rf_surround * center_weight
def spike_timing_dependent_plasticity(self, pre_spike: float,
post_spike: float,
tau: float = 20) -> Dict:
delta_t = pre_spike - post_spike
if abs(delta_t) < tau:
weight_change = np.exp(-abs(delta_t) / tau)
if delta_t > 0:
return {'LTP': weight_change, 'depression': 0}
return {'depression': weight_change, 'LTP': 0}
return {'LTP': 0, 'depression': 0}
class Neuropharmacology:
def __init__(self, drug_class: str):
self.drug_class = drug_class
def receptor_occupancy(self, drug_concentration: float,
kd: float) -> float:
return drug_concentration / (kd + drug_concentration)
def ic50_conversion(self, Ki: float,
agonist_concentration: float) -> float:
return Ki * (1 + agonist_concentration / 1000) # Approximate
def therapeutic_index(self, td50: float,
ed50: float) -> float:
return td50 / ed50
def calculate_ecg_interval(self, heart_rate: float) -> Dict:
qt = 0.4 / np.sqrt(60 / heart_rate)
return {
'QT': qt,
'QTc': qt + 0.1 * (60 / heart_rate - 1)
}
neuro = Electrophysiology("Pyramidal")
E_na = neuro.nernst_potential(145, 15, 1)
E_k = neuro.nernst_potential(5, 150, 1)
print(f"E_Na: {E_na:.1f} mV, E_K: {E_k:.1f} mV")
syn = SynapticTransmission("Glutamatergic")
epsp = syn.calculate_epsp(100, 1000, 50, 2)
print(f"EPSP amplitude: {epsp:.1f} arbitrary units")
Best Practices
- Use appropriate controls in electrophysiological experiments
- Account for temperature effects on neuronal properties
- Consider brain slice vs in vivo preparation differences
- Validate calcium imaging and optical methods properly
- Use proper statistical methods for spike train analysis
- Consider population vs single-neuron activity
- Account for anesthesia effects in in vivo studies
- Use proper virus injection titers for optogenetics
- Validate antibody specificity in neuroanatomy
- Follow ethical guidelines for animal research