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# This file is part of Jaxley, a differentiable neuroscience simulator. Jaxley is | ||
# licensed under the Apache License Version 2.0, see <https://www.apache.org/licenses/> | ||
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from typing import Dict, Optional | ||
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from jaxley.channels import Channel | ||
import jax.numpy as jnp | ||
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from jaxley.solver_gate import save_exp | ||
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class LIF(Channel): | ||
def __init__(self, name: Optional[str] = None): | ||
self.current_is_in_mA_per_cm2 = True | ||
super().__init__(name) | ||
self.channel_params = { | ||
f"{self.name}_gLeak": 0.0003, | ||
f"{self.name}_eLeak": -55, | ||
f"{self.name}_vth": -50, | ||
f"{self.name}_vreset": -55, | ||
f"{self.name}_Tref": 2.0, # refractory period in ms | ||
} | ||
self.channel_states = { | ||
f"{self.name}_rem_tref": 0.0, # time remaining in refractory period | ||
} | ||
self.current_name = f"{self.name}_i" | ||
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def update_states(self, states, dt, v, params): | ||
prefix = self._name | ||
# Decrease refractory time | ||
rem_tref = states[f"{prefix}_rem_tref"] | ||
# Reset refractory timer when spike occurs | ||
new_rem_tref = jnp.where( | ||
v >= params[f"{prefix}_vth"], | ||
params[f"{prefix}_Tref"], # Reset to full refractory period | ||
jnp.maximum(0.0, rem_tref - dt), # Otherwise decrease by dt | ||
) | ||
return {f"{prefix}_rem_tref": new_rem_tref} | ||
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def compute_current(self, states, v, params): | ||
prefix = self._name | ||
eLeak = params[f"{prefix}_eLeak"] | ||
gLeak = params[f"{prefix}_gLeak"] # S/cm^2 | ||
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# Check if in refractory period | ||
is_refractory = states[f"{prefix}_rem_tref"] > 0 | ||
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# If in refractory period, force voltage to reset | ||
# If not in refractory and above threshold, reset voltage | ||
# Otherwise keep current voltage | ||
v_reset = jnp.where( | ||
is_refractory, | ||
params[f"{prefix}_vreset"], | ||
jnp.where(v > params[f"{prefix}_vth"], params[f"{prefix}_vreset"], v), | ||
) | ||
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return -(v_reset - v) + gLeak * (v_reset - eLeak) | ||
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def init_state(self, states, v, params, delta_t): | ||
return {f"{self.name}_rem_tref": jnp.zeros_like(v)} | ||
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class SmoothLIF(Channel): | ||
def __init__(self, name: Optional[str] = None): | ||
self.current_is_in_mA_per_cm2 = True | ||
super().__init__(name) | ||
self.channel_params = { | ||
f"{self.name}_gLeak": 0.0003, | ||
f"{self.name}_eLeak": -55, | ||
f"{self.name}_vth": -50, | ||
f"{self.name}_vreset": -55, | ||
f"{self.name}_Tref": 2.0, # refractory period in ms | ||
f"{self.name}_beta": 1.0e8, # smoothing parameter | ||
} | ||
self.channel_states = { | ||
f"{self.name}_rem_tref": 0.0, # time remaining in refractory period | ||
f"{self.name}_spike_prob": 0.0, # continuous spike probability | ||
} | ||
self.current_name = f"{self.name}_i" | ||
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def sigmoid(self, x, beta): | ||
"""Smooth approximation of the Heaviside step function""" | ||
return 1 / (1 + save_exp(-beta * x)) | ||
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def update_states(self, states, dt, v, params): | ||
prefix = self._name | ||
beta = params[f"{prefix}_beta"] | ||
vth = params[f"{prefix}_vth"] | ||
Tref = params[f"{prefix}_Tref"] | ||
rem_tref = states[f"{prefix}_rem_tref"] | ||
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# Compute smooth spike probability | ||
spike_prob = self.sigmoid(v - vth, beta) | ||
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new_rem_tref = spike_prob * Tref + (1 - spike_prob) * jnp.maximum( | ||
0.0, rem_tref - dt | ||
) | ||
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return {f"{prefix}_rem_tref": new_rem_tref, f"{prefix}_spike_prob": spike_prob} | ||
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def compute_current(self, states, v, params): | ||
prefix = self._name | ||
eLeak = params[f"{prefix}_eLeak"] | ||
gLeak = params[f"{prefix}_gLeak"] | ||
vreset = params[f"{prefix}_vreset"] | ||
rem_tref = states[f"{prefix}_rem_tref"] | ||
Tref = params[f"{prefix}_Tref"] | ||
spike_prob = states[f"{prefix}_spike_prob"] | ||
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# Smooth transition between normal voltage and reset voltage | ||
v_effective = (1 - spike_prob) * v + spike_prob * vreset | ||
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# Blend v_effective towards v_reset based on remaining refractory time | ||
ref_decay = save_exp(-rem_tref / Tref * 1e3) | ||
v_effective = (1 - ref_decay) * vreset + ref_decay * v_effective | ||
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# Compute current with smooth voltage | ||
return -(v_effective - v) + gLeak * (v_effective - eLeak) | ||
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def init_state(self, states, v, params, delta_t): | ||
return { | ||
f"{self.name}_rem_tref": jnp.zeros_like(v), | ||
f"{self.name}_spike_prob": jnp.zeros_like(v), | ||
} |