diff --git a/bindsnet/network/nodes.py b/bindsnet/network/nodes.py index cf8b709cb..eaffcb177 100644 --- a/bindsnet/network/nodes.py +++ b/bindsnet/network/nodes.py @@ -1699,3 +1699,121 @@ def set_batch_size(self, batch_size) -> None: super().set_batch_size(batch_size=batch_size) self.v = self.rest * torch.ones(batch_size, *self.shape, device=self.v.device) self.refrac_count = torch.zeros_like(self.v, device=self.refrac_count.device) + + +class IFNodes_PredInf(Nodes): + # language=rst + """ + Layer of `integrate-and-fire (IF) neurons `_. + """ + + def __init__( + self, + n: Optional[int] = None, + shape: Optional[Iterable[int]] = None, + traces: bool = False, + traces_additive: bool = False, + tc_trace: Union[float, torch.Tensor] = 20.0, + trace_scale: Union[float, torch.Tensor] = 1.0, + sum_input: bool = False, + thresh: Union[float, torch.Tensor] = -52.0, + reset: Union[float, torch.Tensor] = -65.0, + refrac: Union[int, torch.Tensor] = 5, + lbound: float = None, + Fire_only_Once: bool = False, + **kwargs, + ) -> None: + # language=rst + """ + Instantiates a layer of IF neurons. + + :param n: The number of neurons in the layer. + :param shape: The dimensionality of the layer. + :param traces: Whether to record spike traces. + :param traces_additive: Whether to record spike traces additively. + :param tc_trace: Time constant of spike trace decay. + :param trace_scale: Scaling factor for spike trace. + :param sum_input: Whether to sum all inputs. + :param thresh: Spike threshold voltage. + :param reset: Post-spike reset voltage. + :param refrac: Refractory (non-firing) period of the neuron. + :param lbound: Lower bound of the voltage. + :param Fire_only_Once: Whether to allow only one spike per run until reset. + """ + super().__init__( + n=n, + shape=shape, + traces=traces, + traces_additive=traces_additive, + tc_trace=tc_trace, + trace_scale=trace_scale, + sum_input=sum_input, + ) + + self.register_buffer( + "reset", torch.tensor(reset, dtype=torch.float) + ) # Post-spike reset voltage. + self.register_buffer( + "thresh", torch.tensor(thresh, dtype=torch.float) + ) # Spike threshold voltage. + self.register_buffer( + "refrac", torch.tensor(refrac) + ) # Post-spike refractory period. + self.register_buffer("v", torch.FloatTensor()) # Neuron voltages. + self.register_buffer( + "refrac_count", torch.FloatTensor() + ) # Refractory period counters. + + self.Fire_only_Once = Fire_only_Once + if self.Fire_only_Once: + self.refrac = torch.ones_like(self.refrac) * torch.iinfo(torch.int32).max + + + self.lbound = lbound # Lower bound of voltage. + + def forward(self, x: torch.Tensor) -> None: + # language=rst + """ + Runs a single simulation step. + + :param x: Inputs to the layer. + """ + # Integrate input voltages. + self.v += (self.refrac_count <= 0).float() * x + + # Decrement refractory counters. + self.refrac_count -= self.dt + + # Check for spiking neurons. + self.s = self.v >= self.thresh + + # Refractoriness and voltage reset. + self.refrac_count.masked_fill_(self.s, self.refrac) + self.v.masked_fill_(self.s, self.reset) + + # Voltage clipping to lower bound. + if self.lbound is not None: + self.v.masked_fill_(self.v < self.lbound, self.lbound) + + super().forward(x) + + def reset_state_variables(self) -> None: + # language=rst + """ + Resets relevant state variables. + """ + super().reset_state_variables() + self.v.fill_(self.reset) # Neuron voltages. + self.refrac_count.zero_() # Refractory period counters. + + def set_batch_size(self, batch_size) -> None: + # language=rst + """ + Sets mini-batch size. Called when layer is added to a network. + + :param batch_size: Mini-batch size. + """ + super().set_batch_size(batch_size=batch_size) + self.v = self.reset * torch.ones(batch_size, *self.shape, device=self.v.device) + self.refrac_count = torch.zeros_like(self.v, device=self.refrac_count.device) + diff --git a/poetry.lock b/poetry.lock index 3ca31bc66..882bcc724 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,34 +1,5 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. -[[package]] -name = "aiofiles" -version = "22.1.0" -description = "File support for asyncio." -optional = false -python-versions = ">=3.7,<4.0" -files = [ - {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"}, - {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"}, -] - -[[package]] -name = "aiosqlite" -version = "0.20.0" -description = 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b/pyproject.toml index c9a484d27..ab4cb893f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,9 +11,9 @@ keywords = ["spiking", "neural", "networks", "pytorch"] [tool.poetry.dependencies] python = ">=3.9,<3.12" -numpy = "^1.24.3" -scipy = "^1.10.1" -Cython = "^0.29.33" +numpy = "^2" +scipy = "^1" +Cython = "^3" torch = [ {version = "2.3.1", platform = "linux", source = "torch+cu121"}, {version = "2.3.1", platform = "darwin"}, @@ -32,16 +32,16 @@ torchaudio = [ {version = "2.3.1", platform = "win32", source = "torch+cu121"}, {version = "2.3.1", platform = "win_amd64", source = "torch+cu121"}, ] -tensorboardX = "^2.6.2.2" -tqdm = "^4.66.2" -matplotlib = "^3.8.3" +tensorboardX = "^2.6.2" +tqdm = "^4" +matplotlib = "^3.9.0" gymnasium = {extras = ["atari", "accept-rom-license"], version = "^0.29.1"} -scikit-build = "^0.17.6" -scikit-image = "^0.21.0" -scikit-learn = "^1.2.2" -opencv-python = "^4.7.0.72" -pandas = "^2.0.2" -foolbox = "^3.3.3" +scikit-build = "^0.18" +scikit-image = 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