Sep. 3, 2026 : Google released Fruit Fly Neural Model
Sep. 22, 2026 : Let's start coding for Google's Fruit Fly Neural Model with Object Pascal.
Programming an artificial neural network (ANN) based on the Drosophila (fruit fly) connectome using Object Pascal (Delphi or Free Pascal/Lazarus) is highly practical. Because Object Pascal compiles to fast, native machine code and manages memory efficiently, it is excellent for simulating the fruit fly's ~140,000–166,000 neurons and millions of synapses on standard hardware.
The standard approach to modeling a real biological connectome relies on a Leaky Integrate-and-Fire (LIF) network model, mapping physical connections to a directed graph.
- Data Layout Performance: The use of dynamic arrays (
TArray<T>) packed contiguously minimizes cache misses. For massive scaling, consider a flat Sparse Matrix architecture (such as Compressed Sparse Row) to map connections rather than nesting arrays inside records. - The Dataset Pipeline: Real fruit fly dataset connectivity matrices can be fetched from repositories such as FlyWire or Google's connectome data tools. Use a JSON or CSV parsing framework in Delphi (e.g.,
System.JSON) to read the synapse connectivity spreadsheet into your Object Pascal graph engine during initialization. - Parallel Processing: The
UpdateNetworkSteploop can be accelerated across multiple CPU cores usingSystem.Threading.TParallel.Forto scale your simulation cleanly.
If you want to build this out, let me know:
* Will you be importing external connectome files (like CSV/JSON mappings), or setting up synthetic circuits manually?
* Are you targeting real-time simulation metrics, or offline data classification?
I can tailor code extensions or parsing routines directly to your platform needs.
| No. | Subject | Author | Date | Views |
|---|---|---|---|---|
| » |
Let's start coding for Google's Fruit Fly Neural Model with Object Pascal
[1]  | me | 2026.09.22 | 16 |
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unit FlyNeuralNetwork;
interface
uses
System.Generics.Collections, System.SysUtils;
type
// Represents a chemical connection between two neurons
TSynapse = record
TargetNeuronIndex: Integer;
Weight: Single; // Synaptic weight or connection strength
end;
// A biological neuron modeled via Leaky Integrate-and-Fire (LIF)
TNeuron = record
MembranePotential: Single;
Threshold: Single;
Decay: Single; // Leakage coefficient per time-step
RestingPotential: Single;
Synapses: TArray<TSynapse>;
IsSpiking: Boolean; // Output state for the current time-step
end;
TFruitFlyBrain = class
private
FNeurons: TArray<TNeuron>;
procedure ProcessSpikePropagation(SourceIndex: Integer; var NextPotentials: TArray<Single>);
public
constructor Create(NeuronCount: Integer);
procedure AddSynapse(SourceIndex, TargetIndex: Integer; Weight: Single);
procedure UpdateNetworkStep(const ExternalInputs: TArray<Single>);
property Neurons: TArray<TNeuron> read FNeurons;
end;
implementation
{ TFruitFlyBrain }
constructor TFruitFlyBrain.Create(NeuronCount: Integer);
var
I: Integer;
begin
SetLength(FNeurons, NeuronCount);
for I := 0 to High(FNeurons) do
begin
FNeurons[I].MembranePotential := 0.0;
FNeurons[I].RestingPotential := 0.0;
FNeurons[I].Threshold := 1.0;
FNeurons[I].Decay := 0.9; // Keeps 90% voltage if no input exists
FNeurons[I].IsSpiking := False;
SetLength(FNeurons[I].Synapses, 0);
end;
end;
procedure TFruitFlyBrain.AddSynapse(SourceIndex, TargetIndex: Integer; Weight: Single);
var
Len: Integer;
begin
Len := Length(FNeurons[SourceIndex].Synapses);
SetLength(FNeurons[SourceIndex].Synapses, Len + 1);
FNeurons[SourceIndex].Synapses[Len].TargetNeuronIndex := TargetIndex;
FNeurons[SourceIndex].Synapses[Len].Weight := Weight;
end;
procedure TFruitFlyBrain.ProcessSpikePropagation(SourceIndex: Integer; var NextPotentials: TArray<Single>);
var
I: Integer;
SynIdx: Integer;
begin
// Fire neurotransmitters downstream
for I := 0 to High(FNeurons[SourceIndex].Synapses) do
begin
SynIdx := FNeurons[SourceIndex].Synapses[I].TargetNeuronIndex;
NextPotentials[SynIdx] := NextPotentials[SynIdx] + FNeurons[SourceIndex].Synapses[I].Weight;
end;
end;
procedure TFruitFlyBrain.UpdateNetworkStep(const ExternalInputs: TArray<Single>);
var
I: Integer;
NextPotentials: TArray<Single>;
begin
SetLength(NextPotentials, Length(FNeurons));
// 1. Calculate base leakage and integrate external stimulus inputs
for I := 0 to High(FNeurons) do
begin
NextPotentials[I] := FNeurons[I].RestingPotential +
((FNeurons[I].MembranePotential - FNeurons[I].RestingPotential) * FNeurons[I].Decay);
if (I <= High(ExternalInputs)) then
NextPotentials[I] := NextPotentials[I] + ExternalInputs[I];
end;
// 2. Determine which neurons crossed the threshold to spike
for I := 0 to High(FNeurons) do
begin
if FNeurons[I].MembranePotential >= FNeurons[I].Threshold then
begin
FNeurons[I].IsSpiking := True;
// Reset the current spiking neuron's potential
NextPotentials[I] := FNeurons[I].RestingPotential;
// Propagate the electrical signal to connected neurons
ProcessSpikePropagation(I, NextPotentials);
end
else
begin
FNeurons[I].IsSpiking := False;
end;
end;
// 3. Commit the newly calculated state changes
for I := 0 to High(FNeurons) do
begin
FNeurons[I].MembranePotential := NextPotentials[I];
end;
end;
end.