Private user posted an update — Sep 07, 2025 09:50 AM EDT

Bihemispheric neural network # First layer#

Attention mechanism, signal processing modulation=filter+denoising+classification=2output vector=nonlinear patterns/linear pattern=transition to hidden layer/projection neurons

EnhancedBiHemisphericLayer(nn.Module): def init(self, in_channels, out_channels): super().init()

Preprocessing layer - denoising

self.denoise = nn.Sequential( nn.Conv1d(in_channels, in_channels//2, kernel_size=3, padding=1), nn.ReLU(), nn.Dropout(0.1) )

Two hemispheres

self.left_hemisphere = nn.Sequential( nn.Conv1d(in_channels//2, out_channels, kernel_size=5, padding=2), nn.LayerNorm([out_channels, 100]), nn.ReLU() )

self.right_hemisphere = nn.Sequential( nn.Conv1d(in_channels//2, out_channels, kernel_size=3, padding=1), nn.LayerNorm([out_channels, 100]), nn.ReLU() )

Merge hemispheres

self.fusion = nn.Sequential( nn.Conv1d(out_channels*2, out_channels, kernel_size=1), nn.ReLU() )

def forward(self, x):

Preprocess and denoise

cleaned = self.denoise(x)

Parallel processing in two hemispheres

left_out = self.left_hemisphere(cleaned) right_out = self.right_hemisphere(cleaned)

Merge and return

combined = torch.cat([left_out, right_out], dim=1) return self.fusion(combined

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